US20160119806A1 - Systems, methods, and devices having databases and automated reports for electronic spectrum management - Google Patents
Systems, methods, and devices having databases and automated reports for electronic spectrum management Download PDFInfo
- Publication number
- US20160119806A1 US20160119806A1 US14/983,678 US201514983678A US2016119806A1 US 20160119806 A1 US20160119806 A1 US 20160119806A1 US 201514983678 A US201514983678 A US 201514983678A US 2016119806 A1 US2016119806 A1 US 2016119806A1
- Authority
- US
- United States
- Prior art keywords
- signal
- data
- processor
- frequency
- block
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/20—Monitoring; Testing of receivers
- H04B17/27—Monitoring; Testing of receivers for locating or positioning the transmitter
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/20—Monitoring; Testing of receivers
- H04B17/23—Indication means, e.g. displays, alarms, audible means
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/318—Received signal strength
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/14—Spectrum sharing arrangements between different networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0446—Resources in time domain, e.g. slots or frames
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/51—Allocation or scheduling criteria for wireless resources based on terminal or device properties
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
- H04W72/541—Allocation or scheduling criteria for wireless resources based on quality criteria using the level of interference
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
- H04B17/3911—Fading models or fading generators
Definitions
- 14/082,873 is also a continuation-in-part of U.S. application Ser. No. 14/082,916, filed Nov. 18, 2013, which is a continuation of U.S. application Ser. No. 13/912,893, filed Jun. 7, 2013, which claims the benefit of U.S. Application 61/789,758, filed Mar. 15, 2013, each of which is hereby incorporated by reference in its entirety.
- U.S. application Ser. No. 14/082,873 is also a continuation-in-part of U.S. application Ser. No. 14/082,930, filed Nov. 18, 2013, which is a continuation of U.S. application Ser. No. 13/913,013, filed Jun. 7, 2013, which claims the benefit of U.S. Application 61/789,758, filed Mar. 15, 2013, each of which is hereby incorporated by reference in its entirety.
- the present invention relates to spectrum analysis and management for radio frequency signals, and more particularly for automatically identifying signals and devices, comparing and storing data from a multiplicity of devices and automatically generating reports for a wireless communications spectrum.
- Spectrum management includes the process of regulating the use of radio frequencies to promote efficient use and gain net social benefit.
- a problem faced in effective spectrum management is the various numbers of devices emanating wireless signal propagations at different frequencies and across different technological standards. Coupled with the different regulations relating to spectrum usage around the globe effective spectrum management becomes difficult to obtain and at best can only be reached over a long period of time.
- Most spectrum management devices may be categorized into two primary types.
- the first type is a spectral analyzer where a device is specifically fitted to run a ‘scanner’ type receiver that is tailored to provide spectral information for a narrow window of frequencies related to a specific and limited type of communications standard, such as cellular communication standard.
- a specific and limited type of communications standard such as cellular communication standard.
- this type of device is only for a specific use and cannot be used to alleviate the entire needs of the spectrum management community.
- the second type of spectral management device employs a methodology that requires bulky, extremely difficult to use processes, and expensive equipment. In order to attain a broad spectrum management view and complete all the necessary tasks, the device ends up becoming a conglomerate of software and hardware devices that is both hard to use and difficult to maneuver from one location to another.
- the allocating system may be provided with a device for receiving and monitoring information indicative of the presence and location of incumbent radio stations.
- a signal level monitoring system monitors signals transmitted from incumbent radio stations to determine the frequency and degree of RF isolation, with respect to a monitoring antenna of the monitoring system, of the stations.
- the monitoring system includes monitoring antennas, a spectrum analyzer, a device for controlling the spectrum analyzer, and a device for processing and correcting the data produced by the spectrum analyzer.
- Invention is capable of displaying interference threshold and average power.
- U.S. Pat. No. 8,175,539 for “System and method for management of a shared frequency band” by inventors Diener, et al., filed Dec. 22, 2010, describes a system, method, software and related functions for managing activity in a RF band that is shared, both in frequency and time, by signals of multiple types.
- RF energy in the frequency band is captured at one or more devices and/or locations in a region where activity is happening.
- Signals are detected by sampling part or the entire frequency band for time intervals.
- Signal pulse energy in the band is detected and is used to classify signals according to signal type.
- spectrum intelligence Using knowledge of the types of signals occurring in the frequency band and other spectrum activity related statistics (referred to as spectrum intelligence), actions can be taken in a device or network of devices to avoid interfering with other signals, and in general to optimize simultaneous use of the frequency band with the other signals.
- the spectrum intelligence may be used to suggest actions to a device user or network administrator, or to automatically invoke actions in a device or network of devices to maintain desirable performance.
- the terminal comprises a first electro-acoustic transducer for converting a first acoustic signal into an outgoing signal; a wireless transmitter capable of transmitting the outgoing signal to a remote base station; a wireless receiver capable of receiving a plurality of incoming signals from the base station; a second electro-acoustic transducer for converting one of the plurality of incoming signals into a second acoustic signal; a visual display; and a terminal processor for determining a power level for each of the plurality of incoming signals and for contemporaneously displaying an indicium of the power level for each of the plurality of incoming signals onto the visual display.
- Wireless signals are attenuated around office buildings and are not adequately received, thus the invention aims to provide a cheap test tool with a near real-time display.
- the system comprises a non-real-time domain which includes a data generator for supplying a temporally discontinuous data stream which data stream includes signal waveform data and control data defining characteristics of a conversion from the signal waveform data into a RF test signal.
- the temporally discontinuous data stream is fed into a transformer which transforms the temporally discontinuous data stream into a temporally continuous data stream, thus providing a transformation between the non-real-time domain and a real-time domain.
- the real-time domain includes a radio frequency unit which uses the temporally continuous signal data stream as input, and performs the conversion from the signal waveform data into the radio frequency test signal according to the control data.
- U.S. Pat. No. 8,326,240 for “System for specific emitter identification” by inventors Kadambe, et al., filed Sep. 27, 2010, describes an apparatus for identifying a specific emitter in the presence of noise and/or interference including (a) a sensor configured to sense radio frequency signal and noise data, (b) a reference estimation unit configured to estimate a reference signal relating to the signal transmitted by one emitter, (c) a feature estimation unit configured to generate one or more estimates of one or more feature from the reference signal and the signal transmitted by that particular emitter, and (d) an emitter identifier configured to identify the signal transmitted by that particular emitter as belonging to a specific device (e.g., devices using Gaussian Mixture Models and the Bayesian decision engine).
- the apparatus may also include an SINR enhancement unit configured to enhance the SINR of the data before the reference estimation unit estimates the reference signal.
- U.S. Pat. No. 7,835,319 for “System and method for identifying wireless devices using pulse fingerprinting and sequence analysis” by inventor Sugar, filed May 9, 2007, discloses methods for identifying devices that are sources of wireless signals from received radio frequency (RF) energy, and, particularly, sources emitting frequency hopping spread spectrum (FHSS).
- Pulse metric data is generated from the received RF energy and represents characteristics associated thereto.
- the pulses are partitioned into groups based on their pulse metric data such that a group comprises pulses having similarities for at least one item of pulse metric data.
- Sources of the wireless signals are identified based on the partitioning process.
- the partitioning process involves iteratively subdividing each group into subgroups until all resulting subgroups contain pulses determined to be from a single source.
- output data is generated (e.g., a device name for display) that identifies a source of wireless signals for any subgroup that is determined to contain pulses from a single source.
- U.S. Pat. No. 8,131,239 for “Method and apparatus for remote detection of radio-frequency devices” by inventors Walker, et al., filed Aug. 21, 2007, describes methods and apparatus for detecting the presence of electronic communications devices, such as cellular phones, including a complex RF stimulus is transmitted into a target area, and nonlinear reflection signals received from the target area are processed to obtain a response measurement. The response measurement is compared to a pre-determined filter response profile to detect the presence of a radio device having a corresponding filter response characteristic.
- the pre-determined filter response profile comprises a pre-determined band-edge profile, so that comparing the response measurement to a pre-determined filter response profile comprises comparing the response measurement to the pre-determined band-edge profile to detect the presence of a radio device having a corresponding band-edge characteristic.
- Invention aims to be useful in detecting hidden electronic devices.
- a DSA network may monitor spectrum use by cooperative and non-cooperative devices, to dynamically select one or more channels to use for communication while avoiding or reducing interference with other devices.
- a DSA network may include detectors such as a narrow-band detector, wide-band detector, TV detector, radar detector, a wireless microphone detector, or any combination thereof.
- the system uses a modulation method to measure the background signals that eliminates self-generated interference and also identifies the secondary signal to all primary users via on/off amplitude modulation, allowing easy resolution of interference claims.
- the system uses high-processing gain probe waveforms that enable propagation measurements to be made with minimal interference to the primary users.
- the system measures background signals and identifies the types of nearby receivers and modifies the local frequency assignments to minimize interference caused by a secondary system due to non-linear mixing interference and interference caused by out-of-band transmitted signals (phase noise, harmonics, and spurs).
- the system infers a secondary node's elevation and mobility (thus, its probability to cause interference) by analysis of the amplitude of background signals. Elevated or mobile nodes are given more conservative frequency assignments than stationary nodes.
- U.S. Pat. No. 7,424,268 for “System and Method for Management of a Shared Frequency Band” by inventors Diener, et al., filed Apr. 22, 2003 discloses a system, method, software and related functions for managing activity in an unlicensed radio frequency band that is shared, both in frequency and time, by signals of multiple types.
- Signal pulse energy in the band is detected and is used to classify signals according to signal type.
- spectrum intelligence spectrum activity related statistics
- actions can be taken in a device or network of devices to avoid interfering with other signals, and in general to optimize simultaneous use of the frequency band with the other signals.
- the spectrum intelligence may be used to suggest actions to a device user or network administrator, or to automatically invoke actions in a device or network of devices to maintain desirable performance.
- U.S. Pat. No. 8,249,631 for “Transmission power allocation/control method, communication device and program” by inventor Ryo Sawai, filed Jul. 21, 2010, teaches a method for allocating transmission power to a second communication service making secondary usage of a spectrum assigned to a first communication service, in a node which is able to communicate with a secondary usage node.
- the method determines an interference power acceptable for two or more second communication services when the two or more second communication services are operated and allocates the transmission powers to the two or more second communication services.
- U.S. Pat. No. 8,565,811 for “Software-defined radio using multi-core processor” by inventors Tan, et al. discloses a radio control board passing a plurality of digital samples between a memory of a computing device and a radio frequency (RF) transceiver coupled to a system bus of the computing device. Processing of the digital samples is carried out by one or more cores of a multi-core processor to implement a software-defined radio.
- RF radio frequency
- the signal of interest maybe a television signal or a wireless microphone signal using licensed television spectrum.
- wireless communication transmitted in the white space authorizes an initial transmission by a device.
- the wireless communication may include power information for determining a power at which to transmit the initial transmission.
- the initial transmission may be used to request information identifying one or more channels in the white space available for transmitting data.
- NC non-cooperative
- the percentage of power above a first threshold is computed for a channel. Based on the percentage, a signal is classified as a narrowband signal. If the percentage indicates the absence of a narrowband signal, then a lower second threshold is applied to confirm the absence according to the percentage of power above the second threshold.
- the signal is classified as a narrowband signal or pre-classified as a wideband signal based on the percentage. Pre-classified wideband signals are classified as a wideband NC signal or target signal using spectrum masks.
- U.S. Pat. No. 8,494,464 for “Cognitive networked electronic warfare” by inventors Kadambe, et al., filed Sep. 8, 2010, describes an apparatus for sensing and classifying radio communications including sensor units configured to detect RF signals, a signal classifier configured to classify the detected RF signals into a classification, the classification including at least one known signal type and an unknown signal type, a clustering learning algorithm capable of finding clusters of common signals among the previously seen unknown signals; it is then further configured to use these clusters to retrain the signal classifier to recognize these signals as a new signal type, aiming to provide signal identification to better enable electronic attacks and jamming signals.
- a sensing rate is then determined as a function, at least in part, of the information indicating the number of adjacent sensors that are concurrently sensing wireless transmissions from the licensed user of the licensed spectral resource.
- Receiver equipment is then periodically operated at the determined sensing rate, wherein the receiver equipment is configured to detect wireless transmissions from the licensed user of the licensed spectral resource.
- the disclosed methods and apparatus sense signal features by determining a number of spectral density estimates, where each estimate is derived based on reception of the signal by a respective antenna in a system with multiple sensing antennas.
- the spectral density estimates are then combined, and the signal features are sensed based on the combination of the spectral density estimates.
- Invention aims to increase sensing performance by addressing problems associated with Rayleigh fading, which causes signals to be less detectable.
- U.S. Pat. No. 8,151,311 for “System and method of detecting potential video traffic interference” by inventors Huffman, et al., filed Nov. 30, 2007, describes a method of detecting potential video traffic interference at a video head-end of a video distribution network is disclosed and includes detecting, at a video head-end, a signal populating an ultra-high frequency (UHF) white space frequency. The method also includes determining that a strength of the signal is equal to or greater than a threshold signal strength. Further, the method includes sending an alert from the video head-end to a network management system. The alert indicates that the UHF white space frequency is populated by a signal having a potential to interfere with video traffic delivered via the video head-end.
- Cognitive radio technology various sensing mechanisms (energy sensing, National Television System Committee signal sensing, Advanced Television Systems Committee sensing), filtering, and signal reconstruction are disclosed.
- U.S. Pat. No. 8,311,509 for “Detection, communication and control in multimode cellular, TDMA, GSM, spread spectrum, CDMA, OFDM, WiLAN, and WiFi systems” by inventor Feher, filed Oct. 31, 2007, teaches a device for detection of signals, with location finder or location tracker or navigation signal and with Modulation Demodulation (Modem) Format Selectable (MFS) communication signal.
- OFDM Orthogonal Frequency Division Multiplexed
- OFDMA Orthogonal Frequency Division Multiple Access
- Each is used in a Wireless Local Area Network (WLAN) and in Voice over Internet Protocol (VoIP) network.
- Device and location finder with Time Division Multiple Access (TDMA), Global Mobile System (GSM) and spread spectrum Code Division Multiple Access (CDMA) is used in a cellular network.
- Polar and quadrature modulator and two antenna transmitter for transmission of provided processed signal.
- Transmitter with two amplifiers operated in separate radio frequency (RF) bands.
- One transmitter is operated as a Non-Linearly Amplified (NLA) transmitter and the other transmitter is operated as a linearly amplified or linearized amplifier transmitter.
- NLA Non-Linearly Amplified
- the method includes detecting one or more radio frequency (RF) samples; determining burst data by identifying start and stop points of the one or more RF samples; comparing time domain values for an individual burst with time domain values of one or more predetermined RF device profiles; generating a human-readable result indicating whether the individual burst should be assigned to one of the predetermined RF device profiles; and, classifying the individual burst if assigned to one of the predetermined RF device profiles as being a WiFi device or a non-WiFi device with the non-WiFi device being a RF interference source to a wireless network.
- RF radio frequency
- the present invention addresses the longstanding, unmet needs existing in the prior art and commercial sectors to provide solutions to the at least four major problems existing before the present invention, each one that requires near real time results on a continuous scanning of the target environment for the spectrum.
- the present invention provides for near real time automated identification of signals and devices in a wireless communications spectrum, by a multiplicity of apparatus units operable for identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and automatically identifying signals and devices, comparing and storing data from the multiplicity of units and automatically generating reports in a wireless communications spectrum.
- the present invention relates to systems, methods, and devices of the various embodiments enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements, and automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user.
- the protocols of signals may also be identified.
- the modulation of signals, data types carried by the signals, and estimated signal origins may be identified.
- It is an object of this invention is to provide an apparatus for identifying signal emitting devices including: a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in a spectrum associated with wireless communications; and wherein the apparatus is operable to automatically analyze the measured data to identify at least one signal emitting device in near real time from attempted detection and identification of the at least one signal emitting device, and then to identify open space available for wireless communications, based upon the information about the signal emitting device(s) operating in the predetermined spectrum; furthermore, the present invention provides baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data; and wherein each of the apparatus unit(s) is operable for automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- the present invention further provides systems for identifying white space in wireless communications spectrum by detecting and analyzing signals from any signal emitting devices including at least one apparatus, wherein the at least one apparatus is operable for network-based communication with at least one server computer including a database, and/or with at least one other apparatus, but does not require a connection to the at least one server computer to be operable for identifying signal emitting devices; wherein each of the apparatus is operable for identifying signal emitting devices including: a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in a spectrum associated with wireless communications; wherein the apparatus is operable to automatically analyze the measured data to identify at least one signal emitting device in near real time from attempted detection and identification of the at least one signal emitting device, and then to identify open space available for wireless communications, based upon the information about the signal emitting device(s) operating in the predetermined spectrum; all of the foregoing using baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for
- the present invention is further directed to a method for identifying baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data in a wireless communications spectrum
- the present invention provides systems, apparatus, and methods for identifying open space in a wireless communications spectrum using an apparatus having a multiplicity of processors and memory, sensors, and communications transmitters and receivers, all constructed and configured within a housing for automated analysis of detected signals from signal emitting devices, determination of signal duration and other signal characteristics, and automatically generating information relating to device identification, open space, signal optimization, all using baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data within the spectrum for wireless communication, and for automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- FIG. 1 is a system block diagram of a wireless environment suitable for use with the various embodiments.
- FIG. 2A is a block diagram of a spectrum management device according to an embodiment.
- FIG. 2B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to an embodiment.
- FIG. 3 is a process flow diagram illustrating an embodiment method for identifying a signal.
- FIG. 4 is a process flow diagram illustrating an embodiment method for measuring sample blocks of a radio frequency scan.
- FIGS. 5A-5C are a process flow diagram illustrating an embodiment method for determining signal parameters.
- FIG. 6 is a process flow diagram illustrating an embodiment method for displaying signal identifications.
- FIG. 7 is a process flow diagram illustrating an embodiment method for displaying one or more open frequency.
- FIG. 8A is a block diagram of a spectrum management device according to another embodiment.
- FIG. 8B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to another embodiment.
- FIG. 9 is a process flow diagram illustrating an embodiment method for determining protocol data and symbol timing data.
- FIG. 10 is a process flow diagram illustrating an embodiment method for calculating signal degradation data.
- FIG. 11 is a process flow diagram illustrating an embodiment method for displaying signal and protocol identification information.
- FIG. 12A is a block diagram of a spectrum management device according to a further embodiment.
- FIG. 12B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to a further embodiment.
- FIG. 13 is a process flow diagram illustrating an embodiment method for estimating a signal origin based on a frequency difference of arrival.
- FIG. 14 is a process flow diagram illustrating an embodiment method for displaying an indication of an identified data type within a signal.
- FIG. 15 is a process flow diagram illustrating an embodiment method for determining modulation type, protocol data, and symbol timing data.
- FIG. 16 is a process flow diagram illustrating an embodiment method for tracking a signal origin.
- FIG. 17 is a schematic diagram illustrating an embodiment for scanning and finding open space.
- FIG. 18 is a diagram of an embodiment wherein software defined radio nodes are in communication with a master transmitter and device sensing master.
- FIG. 19 is a process flow diagram of an embodiment method of temporally dividing up data into intervals for power usage analysis.
- FIG. 20 is a flow diagram illustrating an embodiment wherein frequency to license matching occurs.
- FIG. 21 is a flow diagram illustrating an embodiment method for reporting power usage information.
- FIG. 22 is a flow diagram illustrating an embodiment method for creating frequency arrays.
- FIG. 23 is a flow diagram illustrating an embodiment method for reframe and aggregating power when producing frequency arrays.
- FIG. 24 is a flow diagram illustrating an embodiment method of reporting license expirations.
- FIG. 25 is a flow diagram illustrating an embodiment method of reporting frequency power use.
- FIG. 26 is a flow diagram illustrating an embodiment method of connecting devices.
- FIG. 27 is a flow diagram illustrating an embodiment method of addressing collisions.
- FIG. 28 is a schematic diagram of an embodiment of the invention illustrating a virtualized computing network and a plurality of distributed devices.
- FIG. 29 is a schematic diagram of an embodiment of the present invention.
- the present invention provides systems, methods, and devices for spectrum analysis and management by identifying, classifying, and cataloging at least one or a multiplicity of signals of interest based on radio frequency measurements and location and other measurements, and using near real-time parallel processing of signals and their corresponding parameters and characteristics in the context of historical and static data for a given spectrum, and more particularly, all using baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data.
- each of the apparatus unit(s) is operable for automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- the systems, methods and apparatus according to the present invention preferably have the ability to detect in near real time, and more preferably to detect, sense, measure, and/or analyze in near real time, and more preferably to perform any near real time operations within about 1 second or less.
- the present invention and its real time functionality described herein uniquely provide and enable the apparatus units to compare to historical data, to update data and/or information, and/or to provide more data and/or information on the open space, on the device that may be occupying the open space, and combinations, in the near real time compared with the historically scanned (15 min to 30 days) data, or historical database information.
- the advanced analytics and reports provided by the present invention enable near real time report generation and display of results and report information on each of the at least one apparatus units, and/or on remote devices as indicated in FIG. 28 , with remote mobile devices and/or computers in addition to the apparatus units, i.e., any other authorized computing devices with access to the remote database(s) and/or the database(s) on the apparatus units, such as by way of example and not limitation, mobile communications devices, smartphones, tablet computers, laptop computers, personal computers, and combinations thereof, each of which having a corresponding display and graphic user interface (GUI).
- GUI graphic user interface
- the present invention systems and methods provide for near real time, automated identification of signals and devices in a wireless communications spectrum, by a multiplicity of apparatus units operable for identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and automatically identifying signals and devices, comparing and storing data from the multiplicity of units and automatically generating reports in a wireless communications spectrum.
- the systems, methods, and devices of the various embodiments enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements.
- signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user.
- the protocols of signals may also be identified.
- the modulation of signals, data types carried by the signals, and estimated signal origins may be identified.
- Embodiments are directed to a spectrum management device that may be configurable to obtain spectrum data over a wide range of wireless communication protocols. Embodiments may also provide for the ability to acquire data from and sending data to database depositories that may be used by a plurality of spectrum management customers.
- a spectrum management device may include a signal spectrum analyzer that may be coupled with a database system and spectrum management interface.
- the device may be portable or may be a stationary installation and may be updated with data to allow the device to manage different spectrum information based on frequency, bandwidth, signal power, time, and location of signal propagation, as well as modulation type and format and to provide signal identification, classification, and geo-location.
- a processor may enable the device to process spectrum power density data as received and to process raw I/Q complex data that may be used for further signal processing, signal identification, and data extraction.
- a spectrum management device may comprise a low noise amplifier that receives a radio frequency (RF) energy from an antenna.
- the antenna may be any antenna structure that is capable of receiving RF energy in a spectrum of interest.
- the low noise amplifier may filter and amplify the RF energy.
- the RF energy may be provided to an RF translator.
- the RF translator may perform a fast Fourier transform (FFT) and either a square magnitude or a fast convolution spectral periodogram function to convert the RF measurements into a spectral representation.
- FFT fast Fourier transform
- the RF translator may also store a timestamp to facilitate calculation of a time of arrival and an angle of arrival.
- the In-Phase and Quadrature (I/Q) data may be provided to a spectral analysis receiver or it may be provided to a sample data store where it may be stored without being processed by a spectral analysis receiver.
- the input RF energy may also be directly digital down-converted and sampled by an analog to digital converter (ADC) to generate complex I/Q data.
- ADC analog to digital converter
- the complex I/Q data may be equalized to remove multipath, fading, white noise and interference from other signaling systems by fast parallel adaptive filter processes. This data may then be used to calculate modulation type and baud rate.
- Complex sampled I/Q data may also be used to measure the signal angle of arrival and time of arrival. Such information as angle of arrival and time of arrival may be used to compute more complex and precise direction finding.
- I/Q sampled data may contain raw signal data that may be used to demodulate and translate signals by streaming them to a signal analyzer or to a real-time demodulator software defined radio that may have the newly identified signal parameters for the signal of interest.
- the inherent nature of the input RF allows for any type of signal to be analyzed and demodulated based on the reconfiguration of the software defined radio interfaces.
- a spectral analysis receiver may be configured to read raw In-Phase (I) and Quadrature (Q) data and either translate directly to spectral data or down convert to an intermediate frequency (IF) up to half the Nyquist sampling rate to analyze the incoming bandwidth of a signal.
- the translated spectral data may include measured values of signal energy, frequency, and time. The measured values provide attributes of the signal under review that may confirm the detection of a particular signal of interest within a spectrum of interest.
- a spectral analysis receiver may have a referenced spectrum input of 0 Hz to 12.4 GHz with capability of fiber optic input for spectrum input up to 60 GHz.
- the spectral analysis receiver may be configured to sample the input RF data by fast analog down-conversion of the RF signal.
- the down-converted signal may then be digitally converted and processed by fast convolution filters to obtain a power spectrum.
- This process may also provide spectrum measurements including the signal power, the bandwidth, the center frequency of the signal as well as a Time of Arrival (TOA) measurement.
- the TOA measurement may be used to create a timestamp of the detected signal and/or to generate a time difference of arrival iterative process for direction finding and fast triangulation of signals.
- the sample data may be provided to a spectrum analysis module.
- the spectrum analysis module may evaluate the sample data to obtain the spectral components of the signal.
- the spectral components of the signal may be obtained by the spectrum analysis module from the raw I/Q data as provided by an RF translator.
- the I/Q data analysis performed by the spectrum analysis module may operate to extract more detailed information about the signal, including by way of example, modulation type (e.g., FM, AM, QPSK, 16QAM, etc.) and/or protocol (e.g., GSM, CDMA, OFDM, LTE, etc.).
- the spectrum analysis module may be configured by a user to obtain specific information about a signal of interest.
- the spectral components of the signal may be obtained from power spectral component data produced by the spectral analysis receiver.
- the spectrum analysis module may provide the spectral components of the signal to a data extraction module.
- the data extraction module may provide the classification and categorization of signals detected in the RF spectrum.
- the data extraction module may also acquire additional information regarding the signal from the spectral components of the signal. For example, the data extraction module may provide modulation type, bandwidth, and possible system in use information.
- the data extraction module may select and organize the extracted spectral components in a format selected by a user.
- the information from the data extraction module may be provided to a spectrum management module.
- the spectrum management module may generate a query to a static database to classify a signal based on its components.
- the information stored in static database may be used to determine the spectral density, center frequency, bandwidth, baud rate, modulation type, protocol (e.g., GSM, CDMA, OFDM, LTE, etc.), system or carrier using licensed spectrum, location of the signal source, and a timestamp of the signal of interest.
- These data points may be provided to a data store for export.
- the data store may be configured to access mapping software to provide the user with information on the location of the transmission source of the signal of interest.
- the static database includes frequency information gathered from various sources including, but not limited to, the Federal Communication Commission, the International Telecommunication Union, and data from users.
- the static database may be an SQL database.
- the data store may be updated, downloaded or merged with other devices or with its main relational database.
- Software API applications may be included to allow database merging with third-party spectrum databases that may only be accessed securely.
- the spectrum management device may be configured in different ways.
- the front end of system may comprise various hardware receivers that may provide In-Phase and Quadrature complex data.
- the front end receiver may include API set commands via which the system software may be configured to interface (i.e., communicate) with a third party receiver.
- the front end receiver may perform the spectral computations using FFT (Fast Fourier Transform) and other DSP (Digital Signal Processing) to generate a fast convolution periodogram that may be re-sampled and averaged to quickly compute the spectral density of the RF environment.
- FFT Fast Fourier Transform
- DSP Digital Signal Processing
- cyclic processes may be used to average and correlate signal information by extracting the changes inside the signal to better identify the signal of interest that is present in the RF space.
- a combination of amplitude and frequency changes may be measured and averaged over the bandwidth time to compute the modulation type and other internal changes, such as changes in frequency offsets, orthogonal frequency division modulation, changes in time (e.g., Time Division Multiplexing), and/or changes in I/Q phase rotation used to compute the baud rate and the modulation type.
- the spectrum management device may have the ability to compute several processes in parallel by use of a multi-core processor and along with several embedded field programmable gate arrays (FPGA).
- FPGA embedded field programmable gate arrays
- Such multi-core processing may allow the system to quickly analyze several signal parameters in the RF environment at one time in order to reduce the amount of time it takes to process the signals.
- the amount of signals computed at once may be determined by their bandwidth requirements.
- the capability of the system may be based on a maximum frequency Fs/2.
- the number of signals to be processed may be allocated based on their respective bandwidths.
- the signal spectrum may be measured to determine its power density, center frequency, bandwidth and location from which the signal is emanating and a best match may be determined based on the signal parameters based on information criteria of the frequency.
- a GPS and direction finding location (DF) system may be incorporated into the spectrum management device and/or available to the spectrum management device. Adding GPS and DF ability may enable the user to provide a location vector using the National Marine Electronics Association's (NMEA) standard form.
- location functionality is incorporated into a specific type of GPS unit, such as a U.S. government issued receiver. The information may be derived from the location presented by the database internal to the device, a database imported into the device, or by the user inputting geo-location parameters of longitude and latitude which may be derived as degrees, minutes and seconds, decimal minutes, or decimal form and translated to the necessary format with the default being ‘decimal’ form.
- This functionality may be incorporated into a GPS unit. The signal information and the signal classification may then be used to locate the signaling device as well as to provide a direction finding capability.
- a type of triangulation using three units as a group antenna configuration performs direction finding by using multilateration.
- multilateration is able to accurately locate an aircraft, vehicle, or stationary emitter by measuring the “Time Difference of Arrival” (TDOA) of a signal from the emitter at three or more receiver sites. If a pulse is emitted from a platform, it will arrive at slightly different times at two spatially separated receiver sites, the TDOA being due to the different distances of each receiver from the platform.
- This location information may then be supplied to a mapping process that utilizes a database of mapping images that are extracted from the database based on the latitude and longitude provided by the geo-location or direction finding device.
- the mapping images may be scanned in to show the points of interest where a signal is either expected to be emanating from based on the database information or from an average taken from the database information and the geo-location calculation performed prior to the mapping software being called.
- the user can control the map to maximize or minimize the mapping screen to get a better view which is more fit to provide information of the signal transmissions.
- the mapping process does not rely on outside mapping software.
- the mapping capability has the ability to generate the map image and to populate a mapping database that may include information from third party maps to meet specific user requirements.
- triangulation and multilateration may utilize a Bayesian type filter that may predict possible movement and future location and operation of devices based on input collected from the TDOA and geolocation processes and the variables from the static database pertaining to the specified signal of interest.
- the Bayesian filter takes the input changes in time difference and its inverse function (i.e., frequency difference) and takes an average changes in signal variation to detect and predict the movement of the signals.
- the signal changes are measured within 1 ns time difference and the filter may also adapt its gradient error calculation to remove unwanted signals that may cause errors due to signal multipath, inter-symbol interference, and other signal noise.
- the changes within a 1 ns time difference for each sample for each unique signal may be recorded.
- the spectrum management device may then perform the inverse and compute and record the frequency difference and phase difference between each sample for each unique signal.
- the spectrum management device may take the same signal and calculates an error based on other input signals coming in within the 1 ns time and may average and filter out the computed error to equalize the signal.
- the spectrum management device may determine the time difference and frequency difference of arrival for that signal and compute the odds of where the signal is emanating from based on the frequency band parameters presented from the spectral analysis and processor computations, and determines the best position from which the signal is transmitted (i.e., origin of the signal).
- FIG. 1 illustrates a wireless environment 100 suitable for use with the various embodiments.
- the wireless environment 100 may include various sources 104 , 106 , 108 , 110 , 112 , and 114 generating various radio frequency (RF) signals 116 , 118 , 120 , 122 , 124 , 126 .
- mobile devices 104 may generate cellular RF signals 116 , such as CDMA, GSM, 3G signals, etc.
- wireless access devices 106 such as Wi-Fi® routers, may generate RF signals 118 , such as Wi-Fi® signals.
- satellites 108 such as communication satellites or GPS satellites, may generate RF signals 120 , such as satellite radio, television, or GPS signals.
- base stations 110 may generate RF signals 122 , such as CDMA, GSM, 3G signals, etc.
- radio towers 112 such as local AM or FM radio stations, may generate RF signals 124 , such as AM or FM radio signals.
- government service provides 114 such as police units, fire fighters, military units, air traffic control towers, etc. may generate RF signals 126 , such as radio communications, tracking signals, etc.
- the various RF signals 116 , 118 , 120 , 122 , 124 , 126 may be generated at different frequencies, power levels, in different protocols, with different modulations, and at different times.
- the various sources 104 , 106 , 108 , 110 , 112 , and 114 may be assigned frequency bands, power limitations, or other restrictions, requirements, and/or licenses by a government spectrum control entity, such as a the FCC. However, with so many different sources 104 , 106 , 108 , 110 , 112 , and 114 generating so many different RF signals 116 , 118 , 120 , 122 , 124 , 126 , overlaps, interference, and/or other problems may occur.
- a spectrum management device 102 in the wireless environment 100 may measure the RF energy in the wireless environment 100 across a wide spectrum and identify the different RF signals 116 , 118 , 120 , 122 , 124 , 126 which may be present in the wireless environment 100 .
- the identification and cataloging of the different RF signals 116 , 118 , 120 , 122 , 124 , 126 which may be present in the wireless environment 100 may enable the spectrum management device 102 to determine available frequencies for use in the wireless environment 100 .
- the spectrum management device 102 may be able to determine if there are available frequencies for use in the wireless environment 100 under certain conditions (i.e., day of week, time of day, power level, frequency band, etc.). In this manner, the RF spectrum in the wireless environment 100 may be managed.
- FIG. 2A is a block diagram of a spectrum management device 202 according to an embodiment.
- the spectrum management device 202 may include an antenna structure 204 configured to receive RF energy expressed in a wireless environment.
- the antenna structure 204 may be any type antenna, and may be configured to optimize the receipt of RF energy across a wide frequency spectrum.
- the antenna structure 204 may be connected to one or more optional amplifiers and/or filters 208 which may boost, smooth, and/or filter the RF energy received by antenna structure 204 before the RF energy is passed to an RF receiver 210 connected to the antenna structure 204 .
- the RF receiver 210 may be configured to measure the RF energy received from the antenna structure 204 and/or optional amplifiers and/or filters 208 .
- the RF receiver 210 may be configured to measure RF energy in the time domain and may convert the RF energy measurements to the frequency domain. In an embodiment, the RF receiver 210 may be configured to generate spectral representation data of the received RF energy.
- the RF receiver 210 may be any type RF receiver, and may be configured to generate RF energy measurements over a range of frequencies, such as 0 kHz to 24 GHz, 9 kHz to 6 GHz, etc. In an embodiment, the frequency scanned by the RF receiver 210 may be user selectable.
- the RF receiver 210 may be connected to a signal processor 214 and may be configured to output RF energy measurements to the signal processor 214 .
- the RF receiver 210 may output raw In-Phase (I) and Quadrature (Q) data to the signal processor 214 .
- the RF receiver 210 may apply signals processing techniques to output complex In-Phase (I) and Quadrature (Q) data to the signal processor 214 .
- the spectrum management device may also include an antenna 206 connected to a location receiver 212 , such as a GPS receiver, which may be connected to the signal processor 214 .
- the location receiver 212 may provide location inputs to the signal processor 214 .
- the signal processor 214 may include a signal detection module 216 , a comparison module 222 , a timing module 224 , and a location module 225 . Additionally, the signal processor 214 may include an optional memory module 226 which may include one or more optional buffers 228 for storing data generated by the other modules of the signal processor 214 .
- the signal detection module 216 may operate to identify signals based on the RF energy measurements received from the RF receiver 210 .
- the signal detection module 216 may include a Fast Fourier Transform (FFT) module 217 which may convert the received RF energy measurements into spectral representation data.
- the signal detection module 216 may include an analysis module 221 which may analyze the spectral representation data to identify one or more signals above a power threshold.
- a power module 220 of the signal detection module 216 may control the power threshold at which signals may be identified. In an embodiment, the power threshold may be a default power setting or may be a user selectable power setting.
- a noise module 219 of the signal detection module 216 may control a signal threshold, such as a noise threshold, at or above which signals may be identified.
- the signal detection module 216 may include a parameter module 218 which may determine one or more signal parameters for any identified signals, such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration, etc.
- the signal processor 214 may include a timing module 224 which may record time information and provide the time information to the signal detection module 216 .
- the signal processor 214 may include a location module 225 which may receive location inputs from the location receiver 212 and determine a location of the spectrum management device 202 . The location of the spectrum management device 202 may be provided to the signal detection module 216 .
- the signal processor 214 may be connected to one or more memory 230 .
- the memory 230 may include multiple databases, such as a history or historical database 232 and characteristics listing 236 , and one or more buffers 240 storing data generated by signal processor 214 . While illustrated as connected to the signal processor 214 the memory 230 may also be on chip memory residing on the signal processor 214 itself.
- the history or historical database 232 may include measured signal data 234 for signals that have been previously identified by the spectrum management device 202 .
- the measured signal data 234 may include the raw RF energy measurements, time stamps, location information, one or more signal parameters for any identified signals, such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration, etc., and identifying information determined from the characteristics listing 236 .
- the history or historical database 232 may be updated as signals are identified by the spectrum management device 202 .
- the characteristic listing 236 may be a database of static signal data 238 .
- the static signal data 238 may include data gathered from various sources including by way of example and not by way of limitation the Federal Communication Commission, the International Telecommunication Union, telecom providers, manufacture data, and data from spectrum management device users.
- Static signal data 238 may include known signal parameters of transmitting devices, such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration, geographic information for transmitting devices, and any other data that may be useful in identifying a signal.
- the static signal data 238 and the characteristic listing 236 may correlate signal parameters and signal identifications.
- the static signal data 238 and characteristic listing 236 may list the parameters of the local fire and emergency communication channel correlated with a signal identification indicating that signal is the local fire and emergency communication channel.
- the signal processor 214 may include a comparison module 222 which may match data generated by the signal detection module 216 with data in the history or historical database 232 and/or characteristic listing 236 .
- the comparison module 222 may receive signal parameters from the signal detection module 216 , such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration, and/or receive parameter from the timing module 224 and/or location module 225 .
- the parameter match module 223 may retrieve data from the history or historical database 232 and/or the characteristic listing 236 and compare the retrieved data to any received parameters to identify matches. Based on the matches the comparison module may identify the signal.
- the signal processor 214 may be optionally connected to a display 242 , an input device 244 , and/or network transceiver 246 .
- the display 242 may be controlled by the signal processor 214 to output spectral representations of received signals, signal characteristic information, and/or indications of signal identifications on the display 242 .
- the input device 244 may be any input device, such as a keyboard and/or knob, mouse, virtual keyboard or even voice recognition, enabling the user of the spectrum management device 202 to input information for use by the signal processor 214 .
- the network transceiver 246 may enable the spectrum management device 202 to exchange data with wired and/or wireless networks, such as to update the characteristic listing 236 and/or upload information from the history or historical database 232 .
- FIG. 2B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device 202 according to an embodiment.
- a receiver 210 may output RF energy measurements, such as I and Q data to a FFT module 252 which may generate a spectral representation of the RF energy measurements which may be output on a display 242 .
- the I and Q data may also be buffered in a buffer 256 and sent to a signal detection module 216 .
- the signal detection module 216 may receive location inputs from a location receiver 212 and use the received I and Q data to detect signals. Data from the signal detection module 216 may be buffered and written into a history or historical database 232 .
- data from the historical database may be used to aid in the detection of signals by the signal detection module 216 .
- the signal parameters of the detected signals may be determined by a signal parameters module 218 using information from the history or historical database 232 and/or a static database 238 listing signal characteristics. Data from the signal parameters module 218 may be stored in the history or historical database 232 and/or sent to the signal detection module 216 and/or display 242 . In this manner, signals may be detected and indications of the signal identification may be displayed to a user of the spectrum management device.
- FIG. 3 illustrates a process flow of an embodiment method 300 for identifying a signal.
- the operations of method 300 may be performed by the processor 214 of a spectrum management device 202 .
- the processor 214 may determine the location of the spectrum management device 202 .
- the processor 214 may determine the location of the spectrum management device 202 based on a location input, such as GPS coordinates, received from a location receiver, such as a GPS receiver 212 .
- the processor 214 may determine the time.
- the time may be the current clock time as determined by the processor 214 and may be a time associated with receiving RF measurements.
- the processor 214 may receive RF energy measurements.
- the processor 214 may receive RF energy measurements from an RF receiver 210 .
- the processor 214 may convert the RF energy measurements to spectral representation data.
- the processor may apply a Fast Fourier Transform (FFT) to the RF energy measurements to convert them to spectral representation data.
- FFT Fast Fourier Transform
- the processor 214 may display the spectral representation data on a display 242 of the spectrum management device 202 , such as in a graph illustrating amplitudes across a frequency spectrum.
- the processor 214 may identify one or more signal above a threshold.
- the processor 214 may analyze the spectral representation data to identify a signal above a power threshold.
- a power threshold may be an amplitude measure selected to distinguish RF energies associated with actual signals from noise.
- the power threshold may be a default value.
- the power threshold may be a user selectable value.
- the processor 214 may determine signal parameters of any identified signal or signals of interest. As examples, the processor 214 may determine signal parameters such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration for the identified signals.
- the processor 214 may store the signal parameters of each identified signal, a location indication, and time indication for each identified signal in a history database 232 .
- a history database 232 may be a database resident in a memory 230 of the spectrum management device 202 which may include data associated with signals actually identified by the spectrum management device.
- the processor 214 may compare the signal parameters of each identified signal to signal parameters in a signal characteristic listing.
- the signal characteristic listing may be a static database 238 stored in the memory 230 of the spectrum management device 202 which may correlate signal parameters and signal identifications.
- the processor 214 may determine whether the signal parameters of the identified signal or signals match signal parameters in the characteristic listing 236 .
- a match may be determined based on the signal parameters being within a specified tolerance of one another. As an example, a center frequency match may be determined when the center frequencies are within plus or minus 1 kHz of each other. In this manner, differences between real world measured conditions of an identified signal and ideal conditions listed in a characteristics listing may be accounted for in identifying matches.
- FIG. 4 illustrates an embodiment method 400 for measuring sample blocks of a radio frequency scan.
- the operations of method 400 may be performed by the processor 214 of a spectrum management device 202 .
- the processor 214 may receive RF energy measurements and convert the RF energy measurements to spectral representation data.
- the processor 214 may determine a frequency range at which to sample the RF spectrum for signals of interest.
- a frequency range may be a frequency range of each sample block to be analyzed for potential signals.
- the frequency range may be 240 kHz.
- the frequency range may be a default value.
- the frequency range may be a user selectable value.
- the processor 214 may determine a number (N) of sample blocks to measure.
- each sample block may be sized to the determined of default frequency range, and the number of sample blocks may be determined by dividing the spectrum of the measured RF energy by the frequency range.
- the processor 214 may assign each sample block a respective frequency range. As an example, if the determined frequency range is 240 kHz, the first sample block may be assigned a frequency range from 0 kHz to 240 kHz, the second sample block may be assigned a frequency range from 240 kHz to 480 kHz, etc.
- the processor 214 may set the lowest frequency range sample block as the current sample block.
- FIGS. 5A, 5B, and 5C illustrate the process flow for an embodiment method 500 for determining signal parameters.
- the operations of method 500 may be performed by the processor 214 of a spectrum management device 202 .
- the processor 214 may receive a noise floor average setting.
- the noise floor average setting may be an average noise level for the environment in which the spectrum management device 202 is operating.
- the noise floor average setting may be a default setting and/or may be user selectable setting.
- the processor 214 may receive the signal power threshold setting.
- the signal power threshold setting may be an amplitude measure selected to distinguish RF energies associated with actual signals from noise.
- the signal power threshold may be a default value and/or may be a user selectable setting.
- the processor 214 may load the next available sample block.
- the sample blocks may be assembled according to the operations of method 400 described above with reference to FIG. 4 .
- the next available sample block may be an oldest in time sample block which has not been analyzed to determine whether signals of interest are present in the sample block.
- the processor 214 may average the amplitude measurements in the sample block.
- determination block 510 the processor 214 may determine whether the average for the sample block is greater than or equal to the noise floor average set in block 502 .
- the sample block may potentially include a signal of interest and in block 512 the processor 214 may reset a measurement counter (C) to 1.
- the measurement counter value indicating which sample within a sample block is under analysis.
- the processor 214 may determine whether the RF measurement of the next frequency sample (C) is greater than the signal power threshold. In this manner, the value of the measurement counter (C) may be used to control which sample RF measurement in the sample block is compared to the signal power threshold.
- C measurement counter
- the C RF measurement e.g., the next RF measurement because the value of the RF measurement counter was incremented
- the cross block flag may be a flag in a memory available to the processor 214 indicating the signal potential spans across two or more sample blocks.
- the slope of a line drawn between the last two RF measurement samples may be used to determine whether the next sample block likely contains further potential signal samples.
- a negative slope may indicate that the signal of interest is fading and may indicate the last sample was the final sample of the signal of interest.
- the slope may not be computed and the next sample block may be analyzed regardless of the slope.
- the processor may perform operations of blocks 506 , 508 , 510 , 512 , 516 , and 518 as discussed above.
- the processor 214 may set the sample stop. As an example, the processor 214 may indicate that a sample end is reached in a memory and/or that a sample is complete in a memory.
- the processor 214 may compute and store complex I and Q data for the stored measurements in the sample.
- the processor 214 may determine a mean of the complex I and Q data.
- the processor 214 may identify the sample as a signal of interest. In an embodiment, the processor 214 may identify the sample as a signal of interest by assigning a signal identifier to the signal, such as a signal number or sample number. In block 548 the processor 214 may determine and store signal parameters for the signal. As an example, the processor 214 may determine and store a frequency peak of the identified signal, a peak power of the identified signal, an average power of the identified signal, a signal bandwidth of the identified signal, and/or a signal duration of the identified signal. In block 552 the processor 214 may clear the cross block flag (or verify that the cross block flag is unset).
- FIG. 6 illustrates a process flow for an embodiment method 600 for displaying signal identifications.
- the operations of method 600 may be performed by a processor 214 of a spectrum management device 202 .
- the processor 214 may compare the signal parameters of the identified signal to signal parameters in a signal characteristic listing 236 .
- the characteristic listing 236 may be a static database separate from the history database 232 , and the characteristic listing 236 may correlate signal parameters with signal identifications.
- the processor 214 may determine whether the signal parameters of the identified signal match any signal parameters in the signal characteristic listing 236 .
- the match in determination 616 may be a match based on a tolerance between the signal parameters of the identified signal and the parameters in the characteristic listing 236 .
- the processor 214 may indicate a match in the history database 232 and in block 622 may display an indication of the signal identification on a display 242 .
- the indication of the signal identification may be a display of the radio call sign of an identified FM radio station signal.
- the processor 214 may display an indication that the signal is an unidentified signal. In this manner, the user may be notified a signal is present in the environment, but that the signal does not match to a signal in the characteristic listing.
- FIG. 7 illustrates a process flow of an embodiment method 700 for displaying one or more open frequency.
- the operations of method 700 may be performed by the processor 214 of a spectrum management device 202 .
- the processor 214 may determine a current location of the spectrum management device 202 .
- the processor 214 may determine the current location of the spectrum management device 202 based on location inputs received from a location receiver 212 , such as GPS coordinates received from a GPS receiver 212 .
- the processor 214 may compare the current location to the stored location value in the historical database 232 .
- the historical or history database 232 may be a database storing information about signals previously actually identified by the spectrum management device 202 .
- the processor 214 may display a plot of one or more of the signals matching the current location. As an example, the processor 214 may compute the average frequency over frequency intervals across a given spectrum and may display a plot of the average frequency over each interval. In block 712 the processor 214 may determine one or more open frequencies at the current location. As an example, the processor 214 may determine one or more open frequencies by determining frequency ranges in which no signals fall or at which the average is below a threshold. In block 714 the processor 214 may display an indication of one or more open frequency on a display 242 of the spectrum management device 202 .
- FIG. 8A is a block diagram of a spectrum management device 802 according to an embodiment.
- Spectrum management device 802 is similar to spectrum management device 202 described above with reference to FIG. 2A , except that spectrum management device 802 may include symbol module 816 and protocol module 806 enabling the spectrum management device 802 to identify the protocol and symbol information associated with an identified signal as well as protocol match module 814 to match protocol information.
- the characteristic listing 236 of spectrum management device 802 may include protocol data 804 , environment data 810 , and noise data 812 and an optimization module 818 may enable the signal processor 214 to provide signal optimization parameters.
- the protocol module 806 may identify the communication protocol (e.g., LTE, CDMA, etc.) associated with a signal of interest. In an embodiment, the protocol module 806 may use data retrieved from the characteristic listing, such as protocol data 804 to help identify the communication protocol.
- the symbol detector module 816 may determine symbol timing information, such as a symbol rate for a signal of interest.
- the protocol module 806 and/or symbol module 816 may provide data to the comparison module 222 .
- the comparison module 222 may include a protocol match module 814 which may attempt to match protocol information for a signal of interest to protocol data 804 in the characteristic listing to identify a signal of interest. Additionally, the protocol module 806 and/or symbol module 816 may store data in the memory module 226 and/or history database 232 . In an embodiment, the protocol module 806 and/or symbol module 816 may use protocol data 804 and/or other data from the characteristic listing 236 to help identify protocols and/or symbol information in signals of interest.
- the optimization module 818 may gather information from the characteristic listing, such as noise figure parameters, antenna hardware parameters, and environmental parameters correlated with an identified signal of interest to calculate a degradation value for the identified signal of interest.
- the optimization module 818 may further control the display 242 to output degradation data enabling a user of the spectrum management device 802 to optimize a signal of interest.
- FIG. 8B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to an embodiment. Only those logical operations illustrated in FIG. 8B different from those described above with reference to FIG. 2B will be discussed.
- As illustrated in FIG. 8B as received time tracking 850 may be applied to the I and Q data from the receiver 210 .
- An additional buffer 851 may further store the I and Q data received and a symbol detector 852 may identify the symbols of a signal of interest and determine the symbol rate.
- a multiple access scheme identifier module 854 may identify whether the signal is part of a multiple access scheme (e.g., CDMA), and a protocol identifier module 856 may attempt to identify the protocol the signal of interested is associated with.
- a multiple access scheme e.g., CDMA
- the multiple access scheme identifier module 854 and protocol identifier module 856 may retrieve data from the static database 238 to aid in the identification of the access scheme and/or protocol.
- the symbol detector module 852 may pass data to the signal parameter and protocol module which may store protocol and symbol information in addition to signal parameter information for signals of interest.
- FIG. 9 illustrates a process flow of an embodiment method 900 for determining protocol data and symbol timing data.
- the operations of method 900 may be performed by the processor 214 of a spectrum management device 802 .
- a mean correlation of each signal may generate a value between 0.0 and 1, and the processor 214 may compare the mean correlation value to a threshold, such as a threshold of 0.75.
- a mean correlation value at or above the threshold may indicate the signals are interrelated while a mean correlation value below the threshold may indicate the signals are not interrelated and may be different signals.
- the mean correlation value may be generated by running a full energy bandwidth correlation of each signal, measuring the values of signal transition for each signal, and for each signal transition running a spectral correlation between signals to generate the mean correlation value.
- the processor 214 may combine signal data for the two or more signals into a signal single entry in the history database.
- FIG. 10 illustrates a process flow of an embodiment method 1000 for calculating signal degradation data.
- the operations of method 1000 may be performed by the processor 214 of a spectrum management device 202 .
- the processor may detect a signal.
- the processor 214 may match the signal to a signal in a static database.
- the processor 214 may determine noise figure parameters based on data in the static database 236 associated with the signal.
- the processor 214 may determine the noise figure of the signal based on parameters of a transmitter outputting the signal according to the static database 236 .
- the processor 214 may determine hardware parameters associated with the signal in the static database 236 .
- the processor 214 may determine hardware parameters such as antenna position, power settings, antenna type, orientation, azimuth, location, gain, and equivalent isotropically radiated power (EIRP) for the transmitter associated with the signal from the static database 236 .
- processor 214 may determine environment parameters associated with the signal in the static database 236 .
- the processor 214 may determine environment parameters such as rain, fog, and/or haze based on a delta correction factor table stored in the static database and a provided precipitation rate (e.g., mm/hr).
- the processor 214 may calculate and store signal degradation data for the detected signal based at least in part on the noise figure parameters, hardware parameters, and environmental parameters.
- the processor 214 may display the degradation data on a display 242 of the spectrum management device 202 .
- the degradation data may be used with measured terrain data of geographic locations stored in the static database to perform pattern distortion, generate propagation and/or next neighbor interference models, determine interference variables, and perform best fit modeling to aide in signal and/or system optimization.
- FIG. 11 illustrates a process flow of an embodiment method 1100 for displaying signal and protocol identification information.
- the operations of method 1100 may be performed by a processor 214 of a spectrum management device 202 .
- the processor 214 may compare the signal parameters and protocol data of an identified signal to signal parameters and protocol data in a history database 232 .
- a history database 232 may be a database storing signal parameters and protocol data for previously identified signals.
- the processor 214 may determine whether there is a match between the signal parameters and protocol data of the identified signal and the signal parameters and protocol data in the history database 232 .
- the processor 214 may compare the signal parameters and protocol data of the identified signal to signal parameters and protocol data in the signal characteristic listing 236 .
- FIG. 12A is a block diagram of a spectrum management device 1202 according to an embodiment.
- Spectrum management device 1202 is similar to spectrum management device 802 described above with reference to FIG. 8A , except that spectrum management device 1202 may include TDOA/FDOA module 1204 and modulation module 1206 enabling the spectrum management device 1202 to identify the modulation type employed by a signal of interest and calculate signal origins.
- the modulation module 1206 may enable the signal processor to determine the modulation applied to signal, such as frequency modulation (e.g., FSK, MSK, etc.) or phase modulation (e.g., BPSK, QPSK, QAM, etc.) as well as to demodulate the signal to identify payload data carried in the signal.
- frequency modulation e.g., FSK, MSK, etc.
- phase modulation e.g., BPSK, QPSK, QAM, etc.
- the modulation module 1206 may use payload data 1221 from the characteristic listing to identify the data types carried in a signal. As examples, upon demodulating a portion of the signal the payload data may enable the processor 214 to determine whether voice data, video data, and/or text based data is present in the signal.
- the TDOA/FDOA module 1204 may enable the signal processor 214 to determine time difference of arrival for signals or interest and/or frequency difference of arrival for signals of interest. Using the TDOA/FDOA information estimates of the origin of a signal may be made and passed to a mapping module 1225 which may control the display 242 to output estimates of a position and/or direction of movement of a signal.
- FIG. 12B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to an embodiment. Only those logical operations illustrated in FIG. 12B different from those described above with reference to FIG. 8B will be discussed.
- Time tracking 850 may additionally include TDOA and/or FDOA (see 1250 ).
- a magnitude squared 1252 operation may be performed on data from the symbol detector 852 to identify whether frequency or phase modulation is present in the signal.
- Phase modulated signals may be identified by the phase modulation 1254 processes and frequency modulated signals may be identified by the frequency modulation processes 1256 .
- the modulation information may be passed to a signal parameters, protocols, and modulation module 1258 .
- FIG. 13 illustrates a process flow of an embodiment method 1300 for estimating a signal origin based on a frequency difference of arrival.
- the operations of method 1300 may be performed by a processor 214 of a spectrum management device 1202 .
- the processor 214 may compute frequency arrivals and phase arrivals for multiple instances of an identified signal.
- the processor 214 may determine frequency difference of arrival for the identified signal based on the computed frequency difference and phase difference.
- the processor may compare the determined frequency difference of arrival for the identified signal to data associated with known emitters in the characteristic listing to estimate an identified signal origin.
- the processor 214 may indicate the estimated identified signal origin on a display of the spectrum management device. As an example, the processor 214 may overlay the estimated origin on a map displayed by the spectrum management device.
- FIG. 14 illustrates a process flow of an embodiment method for displaying an indication of an identified data type within a signal.
- the operations of method 1400 may be performed by a processor 214 of a spectrum management device 1202 .
- the processor 214 may determine the signal parameters for an identified signal of interest.
- the processor 214 may determine the modulation type for the signal of interest.
- the processor 214 may determine the protocol data for the signal of interest.
- the processor 214 may determine the symbol timing for the signal of interest.
- the processor 214 may select a payload scheme based on the determined signal parameters, modulation type, protocol data, and symbol timing. As an example, the payload scheme may indicate how data is transported in a signal.
- data in over the air television broadcasts may be transported differently than data in cellular communications and the signal parameters, modulation type, protocol data, and symbol timing may identify the applicable payload scheme to apply to the signal.
- the processor 214 may apply the selected payload scheme to identify the data type or types within the signal of interest. In this manner, the processor 214 may determine what type of data is being transported in the signal, such as voice data, video data, and/or text based data.
- the processor may store the data type or types.
- the processor 214 may display an indication of the identified data types.
- FIG. 15 illustrates a process flow of an embodiment method 1500 for determining modulation type, protocol data, and symbol timing data.
- Method 1500 is similar to method 900 described above with reference to FIG. 9 , except that modulation type may also be determined.
- the operations of method 1500 may be performed by a processor 214 of a spectrum management device 1202 .
- the processor 214 may perform operations of like numbered blocks of method 900 described above with reference to FIG. 9 .
- the processor may determine and store a modulation type.
- a modulation type may be an indication that the signal is frequency modulated (e.g., FSK, MSK, etc.) or phase modulated (e.g., BPSK, QPSK, QAM, etc.).
- the processor may determine and store protocol data and in block 916 the processor may determine and store timing data.
- a time tracking module such as a TDOA/FDOA module 1204 , may track the frequency repetition interval at which the signal is changing. The frequency repetition interval may also be tracked for a burst signal.
- the spectrum management device may measure the signal environment and set anchors based on information stored in the historic or static database about known transmitter sources and locations.
- the phase information about a signal be extracted using a spectral decomposition correlation equation to measure the angle of arrival (“AOA”) of the signal.
- the processor of the spectrum management device may determine the received power as the Received Signal Strength (“RSS”) and based on the AOA and RSS may measure the frequency difference of arrival.
- RSS Received Signal Strength
- the frequency shift of the received signal may be measured and aggregated over time.
- known transmitted signals may be measured and compared to the RSS to determine frequency shift error.
- the processor of the spectrum management device may compute a cross ambiguity function of aggregated changes in arrival time and frequency of arrival.
- the processor of the spectrum management device may retrieve FFT data for a measured signal and aggregate the data to determine changes in time of arrival and frequency of arrival.
- the signal components of change in frequency of arrival may be averaged through a Kalman filter with a weighted tap filter from 2 to 256 weights to remove measurement error such as noise, multipath interference, etc.
- frequency difference of arrival techniques may be applied when either the emitter of the signal or the spectrum management device are moving or when then emitter of the signal and the spectrum management device are both stationary.
- the determination of the position of the emitter may be made when at least four known other known signal emitters positions are known and signal characteristics may be available.
- a user may provide the four other known emitters and/or may use already in place known emitters, and may use the frequency, bandwidth, power, and distance values of the known emitters and their respective signals.
- frequency deference of arrival techniques may be performed using two known emitters.
- FIG. 16 illustrates an embodiment method for tracking a signal origin.
- the operations of method 1600 may be performed by a processor 214 of a spectrum management device 1202 .
- the processor 214 may determine a time difference of arrival for a signal of interest.
- the processor 214 may determine a frequency difference of arrival for the signal interest.
- the processor 214 may take the inverse of the time difference of arrival to determine the frequency difference of arrival of the signal of interest.
- the processor 214 may identify the location.
- the processor 214 may determine the location based on coordinates provided from a GPS receiver.
- determination block 1608 the processor 214 may determine whether there are at least four known emitters present in the identified location.
- NLOS non-line-of-sight
- location of emitters, time and duration of transmission at a location may be stored in the history database such that historical information may be used to perform and predict movement of signal transmission.
- the environmental factors may be considered to further reduce the measured error and generate a more accurate measurement of the location of the emitter of the signal of interest.
- the processor 214 of spectrum management devices 202 , 802 and 1202 may be any programmable microprocessor, microcomputer or multiple processor chip or chips that can be configured by software instructions (applications) to perform a variety of functions, including the functions of the various embodiments described above. In some devices, multiple processors may be provided, such as one processor dedicated to wireless communication functions and one processor dedicated to running other applications. Typically, software applications may be stored in the internal memory 226 or 230 before they are accessed and loaded into the processor 214 .
- the processor 214 may include internal memory sufficient to store the application software instructions. In many devices the internal memory may be a volatile or nonvolatile memory, such as flash memory, or a mixture of both. For the purposes of this description, a general reference to memory refers to memory accessible by the processor 214 including internal memory or removable memory plugged into the device and memory within the processor 214 itself.
- the present invention provides for systems, methods, and apparatus solutions for device sensing in white space, which improves upon the prior art by identifying sources of signal emission by automatically detecting signals and creating unique signal profiles.
- Device sensing has an important function and applications in military and other intelligence sectors, where identifying the emitter device is crucial for monitoring and surveillance, including specific emitter identification (SEI).
- SEI specific emitter identification
- Signal isolation and device sensing are provided by the present invention: signal isolation and device sensing.
- Signal Isolation is a process whereby a signal is detected, isolated through filtering and amplification, amongst other methods, and key characteristics extracted.
- Device Sensing is a process whereby the detected signals are matched to a device through comparison to device signal profiles and may include applying a confidence level and/or rating to the signal-profile matching. Further, device sensing covers technologies that permit storage of profile comparisons such that future matching can be done with increased efficiency and/or accuracy.
- the present invention systems, methods, and apparatus are constructed and configured functionally to identify any signal emitting device, including by way of example and not limitation, a radio, a cell phone, etc.
- the following functions are included in the present invention: amplifying, filtering, detecting signals through energy detection, waveform-based, spectral correlation-based, radio identification-based, or matched filter method, identifying interference, identifying environmental baseline(s), and/or identify signal characteristics.
- the following functions are included in the present invention: using signal profiling and/or comparison with known database(s) and previously recorded profile(s), identifying the expected device or emitter, stating the level of confidence for the identification, and/or storing profiling and sensing information for improved algorithms and matching.
- the identification of the at least one signal emitting device is accurate to a predetermined degree of confidence between about 80 and about 95 percent, and more preferably between about 80 and about 100 percent.
- the confidence level or degree of confidence is based upon the amount of matching measured data compared with historical data and/or reference data for predetermined frequency and other characteristics.
- the present invention provides for wireless signal-emitting device sensing in the white space based upon a measured signal, and considers the basis of license(s) provided in at least one reference database, preferably the federal communication commission (FCC) and/or other defined database including license listings.
- FCC federal communication commission
- the methods include the steps of providing a device for measuring characteristics of signals from signal emitting devices in a spectrum associated with wireless communications, the characteristics of the measured data from the signal emitting devices including frequency, power, bandwidth, duration, modulation, and combinations thereof; making an assessment or categorization on analog and/or digital signal(s); determining the best fit based on frequency if the measured power spectrum is designated in historical and/or reference data, including but not limited to the FCC or other database(s) for select frequency ranges; determining analog or digital, based on power and sideband combined with frequency allocation; determining a TDM/FDM/CDM signal, based on duration and bandwidth; determining best modulation fit for the desired signal, if the bandwidth and duration match the signal database(s); adding modulation identification to the database; listing possible modulations with best percentage fit, based on the power, bandwidth, frequency, duration, database allocation, and combinations thereof; and identifying at least one signal emitting device from the composite results of the foregoing steps.
- the present invention provides that the phase measurement of the signal is calculated between the difference of the end frequency of the bandwidth and the peak center frequency and the start frequency of the bandwidth and the peak center frequency to get a better measurement of the sideband drop off rate of the signal to help determine the modulation of the signal.
- an apparatus for automatically identifying devices in a spectrum, the apparatus including a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in a spectrum associated with wireless communications; and wherein the apparatus is operable to automatically analyze the measured data to identify at least one signal emitting device in near real time from attempted detection and identification of the at least one signal emitting device.
- the characteristics of signals and measured data from the signal emitting devices include frequency, power, bandwidth, duration, modulation, and combinations thereof.
- the present invention systems including at least one apparatus, wherein the at least one apparatus is operable for network-based communication with at least one server computer including a database, and/or with at least one other apparatus, but does not require a connection to the at least one server computer to be operable for identifying signal emitting devices; wherein each of the apparatus is operable for identifying signal emitting devices including: a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in a spectrum associated with wireless communications; and wherein the apparatus is operable to automatically analyze the measured data to identify at least one signal emitting device in near real time from attempted detection and identification of the at least one signal emitting device.
- the present invention provides for systems, methods, and apparatus solutions for automatically identifying open space, including open space in the white space of a wireless communication spectrum.
- the present invention identifies the open space as the space that is unused and/or seldomly used (and identifies the owner of the licenses for the seldomly used space, if applicable), including unlicensed spectrum, white space, guard bands, and combinations thereof.
- Method steps of the present invention include: automatically obtaining a listing or report of all frequencies in the frequency range; plotting a line and/or graph chart showing power and bandwidth activity; setting frequencies based on a frequency step and/or resolution so that only user-defined frequencies are plotted; generating files, such as by way of example and not limitation, .csv or .pdf files, showing average and/or aggregated values of power, bandwidth and frequency for each derived frequency step; and showing an activity report over time, over day vs. night, over frequency bands if more than one, in white space if requested, in Industrial, Scientific, and Medical (ISM) band or space if requested; and if frequency space is seldomly in that area, then identify and list frequencies and license holders.
- ISM Industrial, Scientific, and Medical
- Additional steps include: automatically scanning the frequency span, wherein a default scan includes a frequency span between about 54 MHz and about 804 MHz; an ISM scan between about 900 MHz and about 2.5 GHz; an ISM scan between about 5 GHz and about 5.8 GHz; and/or a frequency range based upon inputs provided by a user.
- method steps include scanning for an allotted amount of time between a minimum of about 15 minutes up to about 30 days; preferably scanning for allotted times selected from the following: a minimum of about 15 minutes; about 30 minutes; about 1 hour increments; about 5 hour increments; about 10 hour increments; about 24 hours; about 1 day; and about up to 30 days; and combinations thereof.
- the apparatus is configured for automatically scanning for more than about 15 minutes, then the apparatus is preferably set for updating results, including updating graphs and/or reports for an approximately equal amount of time (e.g., every 15 minutes).
- the systems, methods, and apparatus also provide for automatically calculating a percent activity associated with the identified open space on predetermined frequencies and/or ISM bands.
- Preferred embodiments of the present invention provide for sensed and/or measured data received by the at least one apparatus of the present invention, analyzed data, historical data, and/or reference data, change-in-state data, and any updates thereto, are storable on each of the at least one apparatus.
- each apparatus further includes transmitters for sending the sensed and/or measured data received by the at least one apparatus of the present invention, analyzed data, historical data, and/or reference data, change-in-state data, and any updates thereto, are communicated via the network to the at least one remote server computer and its corresponding database(s).
- the server(s) aggregate the data received from the multiplicity of apparatus or devices to produce a composite database for each of the types of data indicated.
- the distributed devices provide the composite database, which allows for additional analytics not possible for individual, isolated apparatus or device units (when not connected in network-based communication), which solves a longstanding, unmet need.
- the aggregation of data from distributed, different apparatus or device units allow for comparison of sample sets of data to compare signal data or information for similar factors, including time(s), day(s), venues, geographic locations or regions, situations, activities, etc., as well as for comparing various signal characteristics with the factors, wherein the signal characteristics and their corresponding sensed and/or measured data, including raw data and change-in-state data, and/or analyzed data from the signal emitting devices include frequency, power, bandwidth, duration, modulation, and combinations thereof.
- the comparisons are conducted in near real time.
- the aggregation of data may provide for information about the same or similar mode from apparatus to apparatus, scanning the same or different frequency ranges, with different factors and/or signal characteristics received and stored in the database(s), both on each apparatus or device unit, and when they are connected in network-based communication for transmission of the data to the at least one remote server.
- the aggregation of data from a multiplicity of units also advantageously provide for continuous, 24 hours/7 days per week scanning, and allows the system to identify sections that exist as well as possibly omitted information or lost data, which may still be considered for comparisons, even if it is incomplete. From a time standpoint, there may not be a linearity with respect to when data is collected or received by the units; rather, the systems and methods of the present invention provide for automated matching of time, i.e., matching timeframes and relative times, even where the environment, activities, and/or context may be different for different units.
- different units may sense and/or measure the same signal from the same signal emitting device in the spectrum, but interference, power, environmental factors, and other factors may present identification issues that preclude one of the at last one apparatus or device units from determining the identity of the signal emitting device with the same degree of certainty or confidence.
- the variation in this data from a multiplicity of units measuring the same signals provides for aggregation and comparison at the remote server using the distributed databases from each unit to generate a variance report in near real time.
- the variance data utilizes value changes or deltas, in the signals rather than complete representations of the signals, either analog or digital, to represent how a signal changes, which advantageously reduces processing times for analysis and for report generation, which provides for near real time generation of the reports, preferably in less than about 5 minutes, including physical printout and/or visual display on GUI;
- the variance reports and variance data include correlation between signal deltas and database deltas to identify and categorize a signal, and also include comparison of spectrum variance to determine spectrum activities for a period of time.
- Variance reports may also include data from more than one of the apparatus units to compare differences or identify variations between them for the same time and same signal targets.
- the database(s) further provide repository database in memory on the apparatus or device units, and/or data from a multiplicity of units are aggregated on at least one remote server to provide an active network with distributed nodes over a region that produce an active or dynamic database of signals, identified devices, identified open space, and combinations thereof, and the nodes may report to or transmit data via network-based communication to a central hub or server.
- This provides for automatically comparing signal emitting devices or their profiles and corresponding sensed or measured data, situations, activities, geographies, times, days, and/or environments, which provides unique composite and comparison data that may be continuously updated, and includes in the near real time reports automatically generated at predetermined times, at user-specified times, on-demand, and/or when data changes occur beyond an expected range.
- Other reports data may include sample size, power usage, average power levels, and interference.
- the significant benefits provided by the present invention automatically generated reports in near real time is that the RF environment may be readily analyzed and communicated using real time or near real time data, so that the reports information is actionable to make changes to improve or optimize signals or to modify the environment for the signals and their corresponding devices. This solves a longstanding unmet need from the prior art.
- FIG. 29 shows a schematic diagram illustrating aspects of the systems, methods and apparatus according to the present invention.
- Each node includes an apparatus or device unit, referenced in the FIG. 1 as “SigSet Device A”, “SigSet Device B”, “SigSet Device C”, and through “SigSet Device N” that are constructed and configured for selective exchange, both transmitting and receiving information over a network connection, either wired or wireless communications, with the master SigDB or database at a remote server location from the units.
- the database aggregating nodes of the apparatus or device units provide a baseline compared with new data, which provide for near real time analysis and results within each of the at least one apparatus or device unit, which calculates and generates results such as signal emitting device identification, identification of open space, signal optimization, and combinations thereof, based upon the particular settings of each of the at least one apparatus or device unit.
- the settings include frequency ranges, location and distance from other units, difference in propagation from one unit to another unit, and combinations thereof, which factor into the final results.
- the present invention systems, methods, and apparatus embodiments provide for leveraging the use of deltas or differentials from the baseline, as well as actual data, to provide onsite sensing, measurement, and analysis for a given environment and spectrum, for each of the at least one apparatus or device unit. Because the present invention provides the at least one processor on each unit to compare signals and signal characteristic differences using compressed data for deltas to provide near real time results, the database storage may further be optimized by storing compressed data and/or deltas, and then decompressing and/or reconstructing the actual signals using the deltas and the baseline. Analytics are also provided using this approach. So then the signals database(s) provide for reduced data storage to the smallest sample set that still provides at least the baseline and the deltas to enable signal reconstruction and analysis to produce the results described according to the present invention.
- the modeling and virtualization analytics enabled by the databases on each of the at least one apparatus or device units independently of the remote server computer, and also provided on the remote server computer from aggregated data provide for “gap filling” for omitted or absent data, and or for reconstruction from deltas.
- a multiplicity of deltas may provide for signal identification, interference identification, neighboring band identification, device identification, signal optimization, and combinations, all in near real time.
- the deltas approach of the present invention which provide for minimization of data sets or sample data sets required for comparisons and/or analytics, i.e., the smallest range of time, frequency, etc. that captures all representative signals and/or deltas associated with the signals, environment conditions, noise, etc.
- the signal database(s) may be represented with visual indications including diagrams, graphs, plots, tables, and combinations thereof, which may be presented directly by the apparatus or device unit to its corresponding display contained within the housing. Also, the signals database(s) provide each apparatus or device unit to receive a first sample data set in a first time period, and receive a second sample data set in a second time period, and receive a N sample data set in a corresponding N time period; to save or store each of the at least two distinct sample data sets; to automatically compare the at least two sample data sets to determine a change-in-state or “delta”. Preferably, the database receives and stores at least the first of the at least two data sets and also stores the delta. The stored delta values provide for quick analytics and regeneration of the actual values of the sample sets from the delta values, which advantageously contributes to the near real time results of the present invention.
- the at least one apparatus is continuously scanning the environment for signals, deltas from prior at least one sample data set, and combinations, which are categorized, classified, and stored in memory.
- the at least one apparatus is continuously scanning the environment for signals, deltas from prior at least one sample data set, and combinations, which are categorized, classified, and stored in memory.
- the systems, methods and apparatus embodiments of the present invention include hardware and software components and requirements to provide for each of the apparatus units to connect and communicate different data they sense, measure, analyze, and/or store on local database(s) in memory on each of the units with the remote server computer and database.
- the master database or “SigDB” is operable to be applied and connect to the units, and may include hardware and software commercially available, for example SQL Server 2012 , and to be applied to provide a user the criteria to upgrade/update their current sever network to the correct configuration that is required to operate and access the SigDB.
- the SigDB is preferably designed, constructed and as a full hardware and software system configuration for the user, including load testing and network security and configuration.
- the SigDB will include a database structure that can sustain a multiplicity of apparatus units' information; provide a method to update the FCC database and/or historical database according a set time (every month/quarter/week, etc.), and in accordance with changes to the FCC.gov databases that are integrated into the database; operable to receive and to download unit data from a remote location through a network connection; be operable to query apparatus unit data stored within the SigDB database server and to query apparatus unit data in ‘present’ time to a particular apparatus unit device for a given ‘present’ time not available in the current SigDB server database; update this information into its own database structure; to keep track of Device Identifications and the information each apparatus unit is collecting including its location; to query the apparatus units based on Device ID or location of device or apparatus unit; to connect to several devices and/or apparatus units on a distributed communications network; to partition data from each apparatus unit or device and differentiate the data from each based on its location and Device ID; to join queries from several devices if a user wants to know
- a GUI interface based on a Web Application software is provided; in one embodiment, the SigDB GUI is provided in any appropriate software, such as by way of example, in Visual Studio using .Net/Asp.Net technology or JavaScript.
- the SigDB GUI preferably operates across cross platform systems with correct browser and operating system (OS) configuration; provides the initial requirements of a History screen in each apparatus unit to access sever information or query a remote apparatus unit containing the desired user information; and, generates .csv and .pdf reports that are useful to the user.
- OS operating system
- Various reports for describing and illustrating with visualization the data and analysis of the device, system and method results from spectrum management activities include at least reports on power usage, RF survey, and/or variance, as well as interference detection, intermodulation detection, uncorrelated licenses, and/or open space identification.
- the systems, methods, and devices of the various embodiments enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements.
- signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user.
- the protocols of signals may also be identified.
- the modulation of signals, devices or device types emitting signals, data types carried by the signals, and estimated signal origins may be identified, and resulting information provided in automatically generated reports.
- Reporting features of the present invention preferably include and support all of the sensing, measurements, analytics, and/or data for each of the at least one apparatus units in systems and methods, including SigDB databases and its advanced analytics.
- the reporting features include: frequency, power, bandwidth, time, and combinations thereof.
- the reports are selected from the group consisting essentially of: variance reports, power usage reports, RF survey reports, signal optimization reports, and combinations thereof.
- Variance reports provide information about the changes in spectrum usage between time periods, between locations, and/or between changes in state.
- Power usage reports provide information about power variables, including but not limited to amplitude, bandwidth, and time, for one or more frequency channels within the spectrum.
- RF survey reports provide detailed information about the spectrum usage and interference for particular signals and/or sites or locations.
- Signal optimization reports include information about interference and options for actions to take to optimize the signal(s) of focus.
- Variance reports provide information on variations within the spectrum.
- the at least one apparatus unit of the system is operable to automatically generate the report following the steps of: after sensing, measuring and/or analyzing the data, group all frequencies by at least one specific frequency range of the measured value collected; automatically check frequencies, and if more than one of the same frequency exists then use the highest and lowest frequency in the group and generate an average frequency, use the highest and lowest power in the group and generate an average power, use highest and lowest bandwidth in the group and generate average bandwidth; group frequencies in order of least to greatest (e.g., ascending order); automatically generate a diagram of Plot Line Graph of Frequency (x-axis) vs Power (y-axis) use FreqAvg and PwrAvg; where multiple same values exist, then automatically apply a smoothing filter and average the graph; set timer and average over time; take a new scan of frequencies and add additional new frequencies that have appeared; average existing
- the at least one apparatus is continuously scanning the environment for signals, deltas from prior at least one sample data set, and combinations, which are categorized, classified, and stored in memory, which are used in automatically generating reports at predetermined times, when specified by a user, and/or at times when updates or deltas are detected or determined. Any and all data, including deltas data, sample data and corresponding sample size, are preferably selectively available for inclusion in the automatically generated reports for near real time data reporting.
- FIG. 17 is a schematic diagram illustrating an embodiment for scanning and finding open space.
- a plurality of nodes are in wireless or wired communication with a software defined radio, which receives information concerning open channels following real-time scanning and access to external database frequency information.
- FIG. 18 is a diagram of an embodiment of the invention wherein software defined radio nodes are in wireless or wired communication with a master transmitter and device sensing master.
- FIG. 19 is a process flow diagram of an embodiment method of temporally dividing up data into intervals for power usage analysis and comparison.
- the data intervals are initially set to seconds, minutes, hours, days and weeks, but can be adjusted to account for varying time periods (e.g., if an overall interval of data is only a week, the data interval divisions would not be weeks).
- the interval slicing of data is used to produce power variance information and reports.
- FIG. 20 is a flow diagram illustrating an embodiment wherein frequency to license matching occurs.
- the center frequency and bandwidth criteria can be checked against a database to check for a license match.
- Both licensed and unlicensed bands can be checked against the frequencies, and, if necessary, non-correlating factors can be marked when a frequency is uncorrelated.
- FIG. 21 is a flow diagram illustrating an embodiment method for reporting power usage information, including locational data, data broken down by time intervals, frequency and power usage information per band, average power distribution, propagation models, atmospheric factors, which is capable of being represented graphical, quantitatively, qualitatively, and overlaid onto a geographic or topographic map.
- FIG. 22 is a flow diagram illustrating an embodiment method for creating frequency arrays. For each initialization, an embodiment of the invention will determine a center frequency, bandwidth, peak power, noise floor level, resolution bandwidth, power and date/time. Start and end frequencies are calculated using the bandwidth and center frequency and like frequencies are aggregated and sorted in order to produce a set of frequency arrays matching power measurements captured in each band.
- FIG. 23 is a flow diagram illustrating an embodiment method for reframe and aggregating power when producing frequency arrays.
- FIG. 24 is a flow diagram illustrating an embodiment method of reporting license expirations by accessing static or FCC databases.
- FIG. 25 is a flow diagram illustrating an embodiment method of reporting frequency power use in graphical, chart, or report format, with the option of adding frequencies from FCC or other databases.
- FIG. 26 is a flow diagram illustrating an embodiment method of connecting devices. After acquiring a GPS location, static and FCC databases are accessed to update license information, if available. A frequency scan will find open spaces and detect interferences and/or collisions. Based on the master device ID, set a random generated token to select channel form available channel model and continually transmit ID channel token. If node device reads ID, it will set itself to channel based on token and device will connect to master device. Master device will then set frequency and bandwidth channel. For each device connected to master, a frequency, bandwidth, and time slot in which to transmit is set. In one embodiment, these steps can be repeated until the max number of devices is connected. As new devices are connected, the device list is updated with channel model and the device is set as active. Disconnected devices are set as inactive. If collision occurs, update channel model and get new token channel. Active scans will search for new or lost devices and update devices list, channel model, and status accordingly. Channel model IDs are actively sent out for new or lost devices.
- FIG. 27 is a flow diagram illustrating an embodiment method of addressing collisions.
- FIG. 28 is a schematic diagram of an embodiment of the invention illustrating a virtualized computing network and a plurality of distributed devices.
- FIG. 28 is a schematic diagram of one embodiment of the present invention, illustrating components of a cloud-based computing system and network for distributed communication therewith by mobile communication devices.
- FIG. 28 illustrates an exemplary virtualized computing system for embodiments of the present invention loyalty and rewards platform.
- FIG. 28 a basic schematic of some of the key components of a virtualized computing (or cloud-based) system according to the present invention are shown.
- the system 2800 comprises at least one remote server computer 2810 with a processing unit 2811 and memory.
- the server 2810 is constructed, configured and coupled to enable communication over a network 2850 .
- the server provides for user interconnection with the server over the network with the at least one apparatus as described hereinabove 2840 positioned remotely from the server.
- Apparatus 2840 includes a memory 2846 , a CPU 2844 , an operating system 2847 , a bus 2842 , an input/output module 2848 , and an output or display 2849 .
- the system is operable for a multiplicity of devices or apparatus embodiments 2860 , 2870 for example, in a client/server architecture, as shown, each having outputs or displays 2869 and 2979 , respectively.
- interconnection through the network 2850 using the at least one device or apparatus for measuring signal emitting devices, each of the at least one apparatus is operable for network-based communication.
- a computer communications network or other suitable architecture may be used.
- the network 2850 may be the Internet, an intranet, or any other network suitable for searching, obtaining, and/or using information and/or communications.
- the system of the present invention further includes an operating system 2812 installed and running on the at least one remote server 2810 , enabling the server 2810 to communicate through network 2850 with the remote, distributed devices or apparatus embodiments as described hereinabove, the server 2810 having a memory 2820 .
- the operating system may be any operating system known in the art that is suitable for network communication.
- FIG. 29 shows a schematic diagram illustrating aspects of the systems, methods and apparatus according to the present invention.
- Each node includes an apparatus or device unit, referenced in the FIG. 29 as “SigSet Device A”, “SigSet Device B”, “SigSet Device C”, and through “SigSet Device N” that are constructed and configured for selective exchange, both transmitting and receiving information over a network connection, either wired or wireless communications, with the master SigDB or database at a remote server location from the units.
- DSP digital signal processor
- ASIC application specific integrated circuit
- FPGA field programmable gate array
- a general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine.
- a processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some steps or methods may be performed by circuitry that is specific to a given function.
- the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable medium or non-transitory processor-readable medium. The steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module which may reside on a non-transitory computer-readable or processor-readable storage medium. Non-transitory computer-readable or processor-readable storage media may be any storage media that may be accessed by a computer or a processor.
- non-transitory computer-readable or processor-readable media may include RAM, ROM, EEPROM, FLASH memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer.
- Disk and disc includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of non-transitory computer-readable and processor-readable media.
- the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non-transitory processor-readable medium and/or computer-readable medium, which may be incorporated into a computer program product.
Abstract
Description
- This application is a continuation of U.S. application Ser. No. 14/504,802 filed Oct. 2, 2014, which is a continuation of U.S. application Ser. No. 14/329,835 filed Jul. 11, 2014, which is a continuation of U.S. application Ser. No. 14/087,441 filed Nov. 22, 2013, which is a continuation-in-part of U.S. application Ser. No. 14/082,873, filed Nov. 18, 2013, which is a continuation of U.S. application Ser. No. 13/912,683, filed Jun. 7, 2013, which claims the benefit of U.S. Application 61/789,758, filed Mar. 15, 2013, each of which is hereby incorporated by reference in its entirety. U.S. application Ser. No. 14/082,873 is also a continuation-in-part of U.S. application Ser. No. 14/082,916, filed Nov. 18, 2013, which is a continuation of U.S. application Ser. No. 13/912,893, filed Jun. 7, 2013, which claims the benefit of U.S. Application 61/789,758, filed Mar. 15, 2013, each of which is hereby incorporated by reference in its entirety. U.S. application Ser. No. 14/082,873 is also a continuation-in-part of U.S. application Ser. No. 14/082,930, filed Nov. 18, 2013, which is a continuation of U.S. application Ser. No. 13/913,013, filed Jun. 7, 2013, which claims the benefit of U.S. Application 61/789,758, filed Mar. 15, 2013, each of which is hereby incorporated by reference in its entirety.
- 1. Field of the Invention
- The present invention relates to spectrum analysis and management for radio frequency signals, and more particularly for automatically identifying signals and devices, comparing and storing data from a multiplicity of devices and automatically generating reports for a wireless communications spectrum.
- 2. Description of the Prior Art
- Generally, it is known in the prior art to provide wireless communications spectrum management for detecting devices for managing the space. Spectrum management includes the process of regulating the use of radio frequencies to promote efficient use and gain net social benefit. A problem faced in effective spectrum management is the various numbers of devices emanating wireless signal propagations at different frequencies and across different technological standards. Coupled with the different regulations relating to spectrum usage around the globe effective spectrum management becomes difficult to obtain and at best can only be reached over a long period of time.
- Another problem facing effective spectrum management is the growing need from spectrum despite the finite amount of spectrum available. Wireless technologies have exponentially grown in recent years. Consequently, available spectrum has become a valuable resource that must be efficiently utilized. Therefore, systems and methods are needed to effectively manage and optimize the available spectrum that is being used.
- Most spectrum management devices may be categorized into two primary types. The first type is a spectral analyzer where a device is specifically fitted to run a ‘scanner’ type receiver that is tailored to provide spectral information for a narrow window of frequencies related to a specific and limited type of communications standard, such as cellular communication standard. Problems arise with these narrowly tailored devices as cellular standards change and/or spectrum use changes impact the spectrum space of these technologies. Changes to the software and hardware for these narrowly tailored devices become too complicated, thus necessitating the need to purchase a totally different and new device. Unfortunately, this type of device is only for a specific use and cannot be used to alleviate the entire needs of the spectrum management community.
- The second type of spectral management device employs a methodology that requires bulky, extremely difficult to use processes, and expensive equipment. In order to attain a broad spectrum management view and complete all the necessary tasks, the device ends up becoming a conglomerate of software and hardware devices that is both hard to use and difficult to maneuver from one location to another.
- While there may be several additional problems associated with current spectrum management devices, at least four major problems exist overall: 1) most devices are built to inherently only handle specific spectrum technologies such as 900 MHz cellular spectrum while not being able to mitigate other technologies that may be interfering or competing with that spectrum, 2) the other spectrum management devices consist of large spectrum analyzers, database systems, and spectrum management software that is expensive, too bulky, and too difficult to manage for a user's basic needs, 3) other spectrum management devices in the prior art require external connectivity to remote databases to perform analysis and provide results or reports with analytics to aid in management of spectrum and/or devices, and 4) other devices of the prior art do not function to provide real-time or near real-time data and analysis to allow for efficient management of the space and/or devices and signals therein.
- Examples of relevant prior art documents include the following:
- U.S. Pat. No. 5,548,809 for “Spectrum sharing communications system and system for monitoring available spectrum” by inventor Paul H. Lemson, filed Jul. 15, 1992, describes a mobile radio communications network with a system for allocating one or more ranges of transmission frequency to the communications network, in order to prevent the network from interfering with received signals of an incumbent radio system. The allocating system may be provided with a device for receiving and monitoring information indicative of the presence and location of incumbent radio stations. A signal level monitoring system monitors signals transmitted from incumbent radio stations to determine the frequency and degree of RF isolation, with respect to a monitoring antenna of the monitoring system, of the stations. The monitoring system includes monitoring antennas, a spectrum analyzer, a device for controlling the spectrum analyzer, and a device for processing and correcting the data produced by the spectrum analyzer. Invention is capable of displaying interference threshold and average power.
- U.S. Pat. No. 8,175,539 for “System and method for management of a shared frequency band” by inventors Diener, et al., filed Dec. 22, 2010, describes a system, method, software and related functions for managing activity in a RF band that is shared, both in frequency and time, by signals of multiple types. RF energy in the frequency band is captured at one or more devices and/or locations in a region where activity is happening. Signals are detected by sampling part or the entire frequency band for time intervals. Signal pulse energy in the band is detected and is used to classify signals according to signal type. Using knowledge of the types of signals occurring in the frequency band and other spectrum activity related statistics (referred to as spectrum intelligence), actions can be taken in a device or network of devices to avoid interfering with other signals, and in general to optimize simultaneous use of the frequency band with the other signals. The spectrum intelligence may be used to suggest actions to a device user or network administrator, or to automatically invoke actions in a device or network of devices to maintain desirable performance.
- U.S. Pat. No. 6,134,445 for “Wireless terminal adapted for measuring signal propagation characteristics” by inventors Gould, et al., filed Jul. 24, 1997, discloses a wireless terminal that functions as both a telecommunications device and as a wireless test tool. Illustratively, the terminal comprises a first electro-acoustic transducer for converting a first acoustic signal into an outgoing signal; a wireless transmitter capable of transmitting the outgoing signal to a remote base station; a wireless receiver capable of receiving a plurality of incoming signals from the base station; a second electro-acoustic transducer for converting one of the plurality of incoming signals into a second acoustic signal; a visual display; and a terminal processor for determining a power level for each of the plurality of incoming signals and for contemporaneously displaying an indicium of the power level for each of the plurality of incoming signals onto the visual display. Wireless signals are attenuated around office buildings and are not adequately received, thus the invention aims to provide a cheap test tool with a near real-time display.
- U.S. Pat. No. 7,162,207 for “System, apparatus, method and computer program for producing signals for testing radio frequency communication devices” by inventors Kursual, et al., filed Jun. 21, 2004, discloses a system, apparatus, a method, and a computer program for producing signals for testing RF communication devices. The system comprises a non-real-time domain which includes a data generator for supplying a temporally discontinuous data stream which data stream includes signal waveform data and control data defining characteristics of a conversion from the signal waveform data into a RF test signal. The temporally discontinuous data stream is fed into a transformer which transforms the temporally discontinuous data stream into a temporally continuous data stream, thus providing a transformation between the non-real-time domain and a real-time domain. The real-time domain includes a radio frequency unit which uses the temporally continuous signal data stream as input, and performs the conversion from the signal waveform data into the radio frequency test signal according to the control data.
- U.S. Pat. No. 8,326,240 for “System for specific emitter identification” by inventors Kadambe, et al., filed Sep. 27, 2010, describes an apparatus for identifying a specific emitter in the presence of noise and/or interference including (a) a sensor configured to sense radio frequency signal and noise data, (b) a reference estimation unit configured to estimate a reference signal relating to the signal transmitted by one emitter, (c) a feature estimation unit configured to generate one or more estimates of one or more feature from the reference signal and the signal transmitted by that particular emitter, and (d) an emitter identifier configured to identify the signal transmitted by that particular emitter as belonging to a specific device (e.g., devices using Gaussian Mixture Models and the Bayesian decision engine). The apparatus may also include an SINR enhancement unit configured to enhance the SINR of the data before the reference estimation unit estimates the reference signal.
- U.S. Pat. No. 7,835,319 for “System and method for identifying wireless devices using pulse fingerprinting and sequence analysis” by inventor Sugar, filed May 9, 2007, discloses methods for identifying devices that are sources of wireless signals from received radio frequency (RF) energy, and, particularly, sources emitting frequency hopping spread spectrum (FHSS). Pulse metric data is generated from the received RF energy and represents characteristics associated thereto. The pulses are partitioned into groups based on their pulse metric data such that a group comprises pulses having similarities for at least one item of pulse metric data. Sources of the wireless signals are identified based on the partitioning process. The partitioning process involves iteratively subdividing each group into subgroups until all resulting subgroups contain pulses determined to be from a single source. At each iteration, subdividing is performed based on different pulse metric data than at a prior iteration. Ultimately, output data is generated (e.g., a device name for display) that identifies a source of wireless signals for any subgroup that is determined to contain pulses from a single source.
- U.S. Pat. No. 8,131,239 for “Method and apparatus for remote detection of radio-frequency devices” by inventors Walker, et al., filed Aug. 21, 2007, describes methods and apparatus for detecting the presence of electronic communications devices, such as cellular phones, including a complex RF stimulus is transmitted into a target area, and nonlinear reflection signals received from the target area are processed to obtain a response measurement. The response measurement is compared to a pre-determined filter response profile to detect the presence of a radio device having a corresponding filter response characteristic. In some embodiments, the pre-determined filter response profile comprises a pre-determined band-edge profile, so that comparing the response measurement to a pre-determined filter response profile comprises comparing the response measurement to the pre-determined band-edge profile to detect the presence of a radio device having a corresponding band-edge characteristic. Invention aims to be useful in detecting hidden electronic devices.
- U.S. Pat. No. 8,369,305 for “Correlating multiple detections of wireless devices without a unique identifier” by inventors Diener, et al., filed Jun. 30, 2008, describes at a plurality of first devices, wireless transmissions are received at different locations in a region where multiple target devices may be emitting, and identifier data is subsequently generated. Similar identifier data associated with received emissions at multiple first devices are grouped together into a cluster record that potentially represents the same target device detected by multiple first devices. Data is stored that represents a plurality of cluster records from identifier data associated with received emissions made over time by multiple first devices. The cluster records are analyzed over time to correlate detections of target devices across multiple first devices. It aims to lessen disruptions caused by devices using the same frequency and to protect data.
- U.S. Pat. No. 8,155,649 for “Method and system for classifying communication signals in a dynamic spectrum access system” by inventors McHenry, et al., filed Aug. 14, 2009, discloses methods and systems for dynamic spectrum access (DSA) in a wireless network wherein a DSA-enabled device may sense spectrum use in a region and, based on the detected spectrum use, select one or more communication channels for use. The devices also may detect one or more other DSA-enabled devices with which they can form DSA networks. A DSA network may monitor spectrum use by cooperative and non-cooperative devices, to dynamically select one or more channels to use for communication while avoiding or reducing interference with other devices. A DSA network may include detectors such as a narrow-band detector, wide-band detector, TV detector, radar detector, a wireless microphone detector, or any combination thereof.
- U.S. Pat. No. RE43,066 for “System and method for reuse of communications spectrum for fixed and mobile applications with efficient method to mitigate interference” by inventor Mark Allen McHenry, filed Dec. 2, 2008, describes a communications system network enabling secondary use of spectrum on a non-interference basis. The system uses a modulation method to measure the background signals that eliminates self-generated interference and also identifies the secondary signal to all primary users via on/off amplitude modulation, allowing easy resolution of interference claims. The system uses high-processing gain probe waveforms that enable propagation measurements to be made with minimal interference to the primary users. The system measures background signals and identifies the types of nearby receivers and modifies the local frequency assignments to minimize interference caused by a secondary system due to non-linear mixing interference and interference caused by out-of-band transmitted signals (phase noise, harmonics, and spurs). The system infers a secondary node's elevation and mobility (thus, its probability to cause interference) by analysis of the amplitude of background signals. Elevated or mobile nodes are given more conservative frequency assignments than stationary nodes.
- U.S. Pat. No. 7,424,268 for “System and Method for Management of a Shared Frequency Band” by inventors Diener, et al., filed Apr. 22, 2003, discloses a system, method, software and related functions for managing activity in an unlicensed radio frequency band that is shared, both in frequency and time, by signals of multiple types. Signal pulse energy in the band is detected and is used to classify signals according to signal type. Using knowledge of the types of signals occurring in the frequency band and other spectrum activity related statistics (referred to as spectrum intelligence), actions can be taken in a device or network of devices to avoid interfering with other signals, and in general to optimize simultaneous use of the frequency band with the other signals. The spectrum intelligence may be used to suggest actions to a device user or network administrator, or to automatically invoke actions in a device or network of devices to maintain desirable performance.
- U.S. Pat. No. 8,249,631 for “Transmission power allocation/control method, communication device and program” by inventor Ryo Sawai, filed Jul. 21, 2010, teaches a method for allocating transmission power to a second communication service making secondary usage of a spectrum assigned to a first communication service, in a node which is able to communicate with a secondary usage node. The method determines an interference power acceptable for two or more second communication services when the two or more second communication services are operated and allocates the transmission powers to the two or more second communication services.
- U.S. Pat. No. 8,094,610 for “Dynamic cellular cognitive system” by inventors Wang, et al., filed Feb. 25, 2009, discloses permitting high quality communications among a diverse set of cognitive radio nodes while minimizing interference to primary and other secondary users by employing dynamic spectrum access in a dynamic cellular cognitive system. Diverse device types interoperate, cooperate, and communicate with high spectrum efficiency and do not require infrastructure to form the network. The dynamic cellular cognitive system can expand to a wider geographical distribution via linking to existing infrastructure.
- U.S. Pat. No. 8,565,811 for “Software-defined radio using multi-core processor” by inventors Tan, et al., discloses a radio control board passing a plurality of digital samples between a memory of a computing device and a radio frequency (RF) transceiver coupled to a system bus of the computing device. Processing of the digital samples is carried out by one or more cores of a multi-core processor to implement a software-defined radio.
- U.S. Pat. No. 8,064,840 for “Method and system for determining spectrum availability within a network” by inventors McHenry, et al., filed Jun. 18, 2009, discloses an invention which determines spectrum holes for a communication network by accumulating the information obtained from previous received signals to determine the presence of a larger spectrum hole that allows a reduced listening period, higher transmit power and a reduced probability of interference with other networks and transmitters.
- U.S. Publication No. 2009/0143019 for “Method and apparatus for distributed spectrum sensing for wireless communication” by inventor Stephen J. Shellhammer, filed Jan. 4, 2008, discloses methods and apparatus for determining if a licensed signal having or exceeding a predetermined field strength is present in a wireless spectrum. The signal of interest maybe a television signal or a wireless microphone signal using licensed television spectrum.
- U.S. Publication No. 2013/0090071 for “Systems and methods for communication in a white space” by inventors Abraham, et al., filed Apr. 3, 2012, discloses systems, methods, and devices to communicate in a white space. In some aspects, wireless communication transmitted in the white space authorizes an initial transmission by a device. The wireless communication may include power information for determining a power at which to transmit the initial transmission. The initial transmission may be used to request information identifying one or more channels in the white space available for transmitting data.
- U.S. Publication No. 2012/0072986 for “Methods for detecting and classifying signals transmitted over a radio frequency spectrum” by inventors Livsics, et al., filed Nov. 1, 2011, discloses a method to classify a signal as non-cooperative (NC) or a target signal. The percentage of power above a first threshold is computed for a channel. Based on the percentage, a signal is classified as a narrowband signal. If the percentage indicates the absence of a narrowband signal, then a lower second threshold is applied to confirm the absence according to the percentage of power above the second threshold. The signal is classified as a narrowband signal or pre-classified as a wideband signal based on the percentage. Pre-classified wideband signals are classified as a wideband NC signal or target signal using spectrum masks.
- U.S. Pat. No. 8,494,464 for “Cognitive networked electronic warfare” by inventors Kadambe, et al., filed Sep. 8, 2010, describes an apparatus for sensing and classifying radio communications including sensor units configured to detect RF signals, a signal classifier configured to classify the detected RF signals into a classification, the classification including at least one known signal type and an unknown signal type, a clustering learning algorithm capable of finding clusters of common signals among the previously seen unknown signals; it is then further configured to use these clusters to retrain the signal classifier to recognize these signals as a new signal type, aiming to provide signal identification to better enable electronic attacks and jamming signals.
- U.S. Publication No. 2011/0059747 for “Sensing Wireless Transmissions From a Licensed User of a Licensed Spectral Resource” by inventors Lindoff, et al., filed Sep. 7, 2009, describes sensing wireless transmissions from a licensed user of a licensed spectral resource includes obtaining information indicating a number of adjacent sensors that are concurrently sensing wireless transmissions from the licensed user of the licensed spectral resource. Such information can be obtained from a main node controlling the sensor and its adjacent sensors, or by the sensor itself (e.g., by means of short-range communication equipment targeting any such adjacent sensors). A sensing rate is then determined as a function, at least in part, of the information indicating the number of adjacent sensors that are concurrently sensing wireless transmissions from the licensed user of the licensed spectral resource. Receiver equipment is then periodically operated at the determined sensing rate, wherein the receiver equipment is configured to detect wireless transmissions from the licensed user of the licensed spectral resource.
- U.S. Pat. No. 8,463,195 for “Methods and apparatus for spectrum sensing of signal features in a wireless channel” by inventor Shellhammer, filed Nov. 13, 2009, discloses methods and apparatus for sensing features of a signal in a wireless communication system are disclosed. The disclosed methods and apparatus sense signal features by determining a number of spectral density estimates, where each estimate is derived based on reception of the signal by a respective antenna in a system with multiple sensing antennas. The spectral density estimates are then combined, and the signal features are sensed based on the combination of the spectral density estimates. Invention aims to increase sensing performance by addressing problems associated with Rayleigh fading, which causes signals to be less detectable.
- U.S. Pat. No. 8,151,311 for “System and method of detecting potential video traffic interference” by inventors Huffman, et al., filed Nov. 30, 2007, describes a method of detecting potential video traffic interference at a video head-end of a video distribution network is disclosed and includes detecting, at a video head-end, a signal populating an ultra-high frequency (UHF) white space frequency. The method also includes determining that a strength of the signal is equal to or greater than a threshold signal strength. Further, the method includes sending an alert from the video head-end to a network management system. The alert indicates that the UHF white space frequency is populated by a signal having a potential to interfere with video traffic delivered via the video head-end. Cognitive radio technology, various sensing mechanisms (energy sensing, National Television System Committee signal sensing, Advanced Television Systems Committee sensing), filtering, and signal reconstruction are disclosed.
- U.S. Pat. No. 8,311,509 for “Detection, communication and control in multimode cellular, TDMA, GSM, spread spectrum, CDMA, OFDM, WiLAN, and WiFi systems” by inventor Feher, filed Oct. 31, 2007, teaches a device for detection of signals, with location finder or location tracker or navigation signal and with Modulation Demodulation (Modem) Format Selectable (MFS) communication signal. Processor for processing a digital signal into cross-correlated in-phase and quadrature-phase filtered signal and for processing a voice signal into Orthogonal Frequency Division Multiplexed (OFDM) or Orthogonal Frequency Division Multiple Access (OFDMA) signal. Each is used in a Wireless Local Area Network (WLAN) and in Voice over Internet Protocol (VoIP) network. Device and location finder with Time Division Multiple Access (TDMA), Global Mobile System (GSM) and spread spectrum Code Division Multiple Access (CDMA) is used in a cellular network. Polar and quadrature modulator and two antenna transmitter for transmission of provided processed signal. Transmitter with two amplifiers operated in separate radio frequency (RF) bands. One transmitter is operated as a Non-Linearly Amplified (NLA) transmitter and the other transmitter is operated as a linearly amplified or linearized amplifier transmitter.
- U.S. Pat. No. 8,514,729 for “Method and system for analyzing RF signals in order to detect and classify actively transmitting RF devices” by inventor Blackwell, filed Apr. 3, 2009, discloses methods and apparatuses to analyze RF signals in order to detect and classify RF devices in wireless networks are described. The method includes detecting one or more radio frequency (RF) samples; determining burst data by identifying start and stop points of the one or more RF samples; comparing time domain values for an individual burst with time domain values of one or more predetermined RF device profiles; generating a human-readable result indicating whether the individual burst should be assigned to one of the predetermined RF device profiles; and, classifying the individual burst if assigned to one of the predetermined RF device profiles as being a WiFi device or a non-WiFi device with the non-WiFi device being a RF interference source to a wireless network.
- However, none of the prior art references provide solutions to the limitations and longstanding unmet needs existing in this area for automatically identifying open space in a wireless communications spectrum. Thus, there remains a need for automated identification of open space, identification of signal emitting devices, and for automated comparisons and analysis, for storing data, and for automatically generating reports in a wireless communications spectrum in near real time.
- The present invention addresses the longstanding, unmet needs existing in the prior art and commercial sectors to provide solutions to the at least four major problems existing before the present invention, each one that requires near real time results on a continuous scanning of the target environment for the spectrum.
- The present invention provides for near real time automated identification of signals and devices in a wireless communications spectrum, by a multiplicity of apparatus units operable for identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and automatically identifying signals and devices, comparing and storing data from the multiplicity of units and automatically generating reports in a wireless communications spectrum.
- The present invention relates to systems, methods, and devices of the various embodiments enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements, and automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports. In an embodiment, signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user. In another embodiment, the protocols of signals may also be identified. In a further embodiment, the modulation of signals, data types carried by the signals, and estimated signal origins may be identified.
- It is an object of this invention is to provide an apparatus for identifying signal emitting devices including: a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in a spectrum associated with wireless communications; and wherein the apparatus is operable to automatically analyze the measured data to identify at least one signal emitting device in near real time from attempted detection and identification of the at least one signal emitting device, and then to identify open space available for wireless communications, based upon the information about the signal emitting device(s) operating in the predetermined spectrum; furthermore, the present invention provides baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data; and wherein each of the apparatus unit(s) is operable for automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- The present invention further provides systems for identifying white space in wireless communications spectrum by detecting and analyzing signals from any signal emitting devices including at least one apparatus, wherein the at least one apparatus is operable for network-based communication with at least one server computer including a database, and/or with at least one other apparatus, but does not require a connection to the at least one server computer to be operable for identifying signal emitting devices; wherein each of the apparatus is operable for identifying signal emitting devices including: a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in a spectrum associated with wireless communications; wherein the apparatus is operable to automatically analyze the measured data to identify at least one signal emitting device in near real time from attempted detection and identification of the at least one signal emitting device, and then to identify open space available for wireless communications, based upon the information about the signal emitting device(s) operating in the predetermined spectrum; all of the foregoing using baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data; and wherein each of the apparatus unit(s) is operable for automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- The present invention is further directed to a method for identifying baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data in a wireless communications spectrum including the steps of: providing a device for measuring characteristics of signals from signal emitting devices in a spectrum associated with wireless communications, with measured data characteristics including frequency, power, bandwidth, duration, modulation, and combinations thereof; the device including a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals within the spectrum; and further including the following steps performed within the device housing: assessing whether the measured data includes analog and/or digital signal(s); determining a best fit based on frequency, if the measured power spectrum is designated in an historical or a reference database(s) for frequency ranges; automatically determining a category for either analog or digital signals, based on power and sideband combined with frequency allocation; determining a TDM/FDM/CDM signal, based on duration and bandwidth; identifying at least one signal emitting device from the composite results of the foregoing steps; and then automatically identifying the open space available for wireless communications, based upon the information about the signal emitting device(s) operating in the predetermined spectrum; all using baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data; and automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- Additionally, the present invention provides systems, apparatus, and methods for identifying open space in a wireless communications spectrum using an apparatus having a multiplicity of processors and memory, sensors, and communications transmitters and receivers, all constructed and configured within a housing for automated analysis of detected signals from signal emitting devices, determination of signal duration and other signal characteristics, and automatically generating information relating to device identification, open space, signal optimization, all using baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data within the spectrum for wireless communication, and for automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- These and other aspects of the present invention will become apparent to those skilled in the art after a reading of the following description of the preferred embodiment when considered with the drawings, as they support the claimed invention.
- The accompanying drawings, which are incorporated herein and constitute part of this specification, illustrate exemplary embodiments of the invention, and together with the general description given above and the detailed description given below, serve to explain the features of the invention.
-
FIG. 1 is a system block diagram of a wireless environment suitable for use with the various embodiments. -
FIG. 2A is a block diagram of a spectrum management device according to an embodiment. -
FIG. 2B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to an embodiment. -
FIG. 3 is a process flow diagram illustrating an embodiment method for identifying a signal. -
FIG. 4 is a process flow diagram illustrating an embodiment method for measuring sample blocks of a radio frequency scan. -
FIGS. 5A-5C are a process flow diagram illustrating an embodiment method for determining signal parameters. -
FIG. 6 is a process flow diagram illustrating an embodiment method for displaying signal identifications. -
FIG. 7 is a process flow diagram illustrating an embodiment method for displaying one or more open frequency. -
FIG. 8A is a block diagram of a spectrum management device according to another embodiment. -
FIG. 8B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to another embodiment. -
FIG. 9 is a process flow diagram illustrating an embodiment method for determining protocol data and symbol timing data. -
FIG. 10 is a process flow diagram illustrating an embodiment method for calculating signal degradation data. -
FIG. 11 is a process flow diagram illustrating an embodiment method for displaying signal and protocol identification information. -
FIG. 12A is a block diagram of a spectrum management device according to a further embodiment. -
FIG. 12B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to a further embodiment. -
FIG. 13 is a process flow diagram illustrating an embodiment method for estimating a signal origin based on a frequency difference of arrival. -
FIG. 14 is a process flow diagram illustrating an embodiment method for displaying an indication of an identified data type within a signal. -
FIG. 15 is a process flow diagram illustrating an embodiment method for determining modulation type, protocol data, and symbol timing data. -
FIG. 16 is a process flow diagram illustrating an embodiment method for tracking a signal origin. -
FIG. 17 is a schematic diagram illustrating an embodiment for scanning and finding open space. -
FIG. 18 is a diagram of an embodiment wherein software defined radio nodes are in communication with a master transmitter and device sensing master. -
FIG. 19 is a process flow diagram of an embodiment method of temporally dividing up data into intervals for power usage analysis. -
FIG. 20 is a flow diagram illustrating an embodiment wherein frequency to license matching occurs. -
FIG. 21 is a flow diagram illustrating an embodiment method for reporting power usage information. -
FIG. 22 is a flow diagram illustrating an embodiment method for creating frequency arrays. -
FIG. 23 is a flow diagram illustrating an embodiment method for reframe and aggregating power when producing frequency arrays. -
FIG. 24 is a flow diagram illustrating an embodiment method of reporting license expirations. -
FIG. 25 is a flow diagram illustrating an embodiment method of reporting frequency power use. -
FIG. 26 is a flow diagram illustrating an embodiment method of connecting devices. -
FIG. 27 is a flow diagram illustrating an embodiment method of addressing collisions. -
FIG. 28 is a schematic diagram of an embodiment of the invention illustrating a virtualized computing network and a plurality of distributed devices. -
FIG. 29 is a schematic diagram of an embodiment of the present invention. - Referring now to the drawings in general, the illustrations are for the purpose of describing at least one preferred embodiment and/or examples of the invention and are not intended to limit the invention thereto. Various embodiments are described in detail with reference to the accompanying drawings. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like parts. References made to particular examples and implementations are for illustrative purposes, and are not intended to limit the scope of the invention or the claims.
- The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations.
- The present invention provides systems, methods, and devices for spectrum analysis and management by identifying, classifying, and cataloging at least one or a multiplicity of signals of interest based on radio frequency measurements and location and other measurements, and using near real-time parallel processing of signals and their corresponding parameters and characteristics in the context of historical and static data for a given spectrum, and more particularly, all using baseline data and changes in state for compressed data to enable near real time analytics and results for individual units and for aggregated units for making unique comparisons of data. Also preferably, each of the apparatus unit(s) is operable for automatically generating reports of the advanced analytics in near real time, and for displaying resulting data and reports.
- The systems, methods and apparatus according to the present invention preferably have the ability to detect in near real time, and more preferably to detect, sense, measure, and/or analyze in near real time, and more preferably to perform any near real time operations within about 1 second or less. Advantageously, the present invention and its real time functionality described herein uniquely provide and enable the apparatus units to compare to historical data, to update data and/or information, and/or to provide more data and/or information on the open space, on the device that may be occupying the open space, and combinations, in the near real time compared with the historically scanned (15 min to 30 days) data, or historical database information. Also, the advanced analytics and reports provided by the present invention enable near real time report generation and display of results and report information on each of the at least one apparatus units, and/or on remote devices as indicated in
FIG. 28 , with remote mobile devices and/or computers in addition to the apparatus units, i.e., any other authorized computing devices with access to the remote database(s) and/or the database(s) on the apparatus units, such as by way of example and not limitation, mobile communications devices, smartphones, tablet computers, laptop computers, personal computers, and combinations thereof, each of which having a corresponding display and graphic user interface (GUI). - The present invention systems and methods provide for near real time, automated identification of signals and devices in a wireless communications spectrum, by a multiplicity of apparatus units operable for identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and automatically identifying signals and devices, comparing and storing data from the multiplicity of units and automatically generating reports in a wireless communications spectrum.
- The systems, methods, and devices of the various embodiments enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements. In an embodiment, signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user. In another embodiment, the protocols of signals may also be identified. In a further embodiment, the modulation of signals, data types carried by the signals, and estimated signal origins may be identified.
- Embodiments are directed to a spectrum management device that may be configurable to obtain spectrum data over a wide range of wireless communication protocols. Embodiments may also provide for the ability to acquire data from and sending data to database depositories that may be used by a plurality of spectrum management customers.
- In one embodiment, a spectrum management device may include a signal spectrum analyzer that may be coupled with a database system and spectrum management interface. The device may be portable or may be a stationary installation and may be updated with data to allow the device to manage different spectrum information based on frequency, bandwidth, signal power, time, and location of signal propagation, as well as modulation type and format and to provide signal identification, classification, and geo-location. A processor may enable the device to process spectrum power density data as received and to process raw I/Q complex data that may be used for further signal processing, signal identification, and data extraction.
- In an embodiment, a spectrum management device may comprise a low noise amplifier that receives a radio frequency (RF) energy from an antenna. The antenna may be any antenna structure that is capable of receiving RF energy in a spectrum of interest. The low noise amplifier may filter and amplify the RF energy. The RF energy may be provided to an RF translator. The RF translator may perform a fast Fourier transform (FFT) and either a square magnitude or a fast convolution spectral periodogram function to convert the RF measurements into a spectral representation. In an embodiment, the RF translator may also store a timestamp to facilitate calculation of a time of arrival and an angle of arrival. The In-Phase and Quadrature (I/Q) data may be provided to a spectral analysis receiver or it may be provided to a sample data store where it may be stored without being processed by a spectral analysis receiver. The input RF energy may also be directly digital down-converted and sampled by an analog to digital converter (ADC) to generate complex I/Q data. The complex I/Q data may be equalized to remove multipath, fading, white noise and interference from other signaling systems by fast parallel adaptive filter processes. This data may then be used to calculate modulation type and baud rate. Complex sampled I/Q data may also be used to measure the signal angle of arrival and time of arrival. Such information as angle of arrival and time of arrival may be used to compute more complex and precise direction finding. In addition, they may be used to apply geo-location techniques. Data may be collected from known signals or unknown signals and time spaced in order to provide expedient information. I/Q sampled data may contain raw signal data that may be used to demodulate and translate signals by streaming them to a signal analyzer or to a real-time demodulator software defined radio that may have the newly identified signal parameters for the signal of interest. The inherent nature of the input RF allows for any type of signal to be analyzed and demodulated based on the reconfiguration of the software defined radio interfaces.
- A spectral analysis receiver may be configured to read raw In-Phase (I) and Quadrature (Q) data and either translate directly to spectral data or down convert to an intermediate frequency (IF) up to half the Nyquist sampling rate to analyze the incoming bandwidth of a signal. The translated spectral data may include measured values of signal energy, frequency, and time. The measured values provide attributes of the signal under review that may confirm the detection of a particular signal of interest within a spectrum of interest. In an embodiment, a spectral analysis receiver may have a referenced spectrum input of 0 Hz to 12.4 GHz with capability of fiber optic input for spectrum input up to 60 GHz.
- In an embodiment, the spectral analysis receiver may be configured to sample the input RF data by fast analog down-conversion of the RF signal. The down-converted signal may then be digitally converted and processed by fast convolution filters to obtain a power spectrum. This process may also provide spectrum measurements including the signal power, the bandwidth, the center frequency of the signal as well as a Time of Arrival (TOA) measurement. The TOA measurement may be used to create a timestamp of the detected signal and/or to generate a time difference of arrival iterative process for direction finding and fast triangulation of signals. In an embodiment, the sample data may be provided to a spectrum analysis module. In an embodiment, the spectrum analysis module may evaluate the sample data to obtain the spectral components of the signal.
- In an embodiment, the spectral components of the signal may be obtained by the spectrum analysis module from the raw I/Q data as provided by an RF translator. The I/Q data analysis performed by the spectrum analysis module may operate to extract more detailed information about the signal, including by way of example, modulation type (e.g., FM, AM, QPSK, 16QAM, etc.) and/or protocol (e.g., GSM, CDMA, OFDM, LTE, etc.). In an embodiment, the spectrum analysis module may be configured by a user to obtain specific information about a signal of interest. In an alternate embodiment, the spectral components of the signal may be obtained from power spectral component data produced by the spectral analysis receiver.
- In an embodiment, the spectrum analysis module may provide the spectral components of the signal to a data extraction module. The data extraction module may provide the classification and categorization of signals detected in the RF spectrum. The data extraction module may also acquire additional information regarding the signal from the spectral components of the signal. For example, the data extraction module may provide modulation type, bandwidth, and possible system in use information. In another embodiment, the data extraction module may select and organize the extracted spectral components in a format selected by a user.
- The information from the data extraction module may be provided to a spectrum management module. The spectrum management module may generate a query to a static database to classify a signal based on its components. For example, the information stored in static database may be used to determine the spectral density, center frequency, bandwidth, baud rate, modulation type, protocol (e.g., GSM, CDMA, OFDM, LTE, etc.), system or carrier using licensed spectrum, location of the signal source, and a timestamp of the signal of interest. These data points may be provided to a data store for export. In an embodiment and as more fully described below, the data store may be configured to access mapping software to provide the user with information on the location of the transmission source of the signal of interest. In an embodiment, the static database includes frequency information gathered from various sources including, but not limited to, the Federal Communication Commission, the International Telecommunication Union, and data from users. As an example, the static database may be an SQL database. The data store may be updated, downloaded or merged with other devices or with its main relational database. Software API applications may be included to allow database merging with third-party spectrum databases that may only be accessed securely.
- In the various embodiments, the spectrum management device may be configured in different ways. In an embodiment, the front end of system may comprise various hardware receivers that may provide In-Phase and Quadrature complex data. The front end receiver may include API set commands via which the system software may be configured to interface (i.e., communicate) with a third party receiver. In an embodiment, the front end receiver may perform the spectral computations using FFT (Fast Fourier Transform) and other DSP (Digital Signal Processing) to generate a fast convolution periodogram that may be re-sampled and averaged to quickly compute the spectral density of the RF environment.
- In an embodiment, cyclic processes may be used to average and correlate signal information by extracting the changes inside the signal to better identify the signal of interest that is present in the RF space. A combination of amplitude and frequency changes may be measured and averaged over the bandwidth time to compute the modulation type and other internal changes, such as changes in frequency offsets, orthogonal frequency division modulation, changes in time (e.g., Time Division Multiplexing), and/or changes in I/Q phase rotation used to compute the baud rate and the modulation type. In an embodiment, the spectrum management device may have the ability to compute several processes in parallel by use of a multi-core processor and along with several embedded field programmable gate arrays (FPGA). Such multi-core processing may allow the system to quickly analyze several signal parameters in the RF environment at one time in order to reduce the amount of time it takes to process the signals. The amount of signals computed at once may be determined by their bandwidth requirements. Thus, the capability of the system may be based on a maximum frequency Fs/2. The number of signals to be processed may be allocated based on their respective bandwidths. In another embodiment, the signal spectrum may be measured to determine its power density, center frequency, bandwidth and location from which the signal is emanating and a best match may be determined based on the signal parameters based on information criteria of the frequency.
- In another embodiment, a GPS and direction finding location (DF) system may be incorporated into the spectrum management device and/or available to the spectrum management device. Adding GPS and DF ability may enable the user to provide a location vector using the National Marine Electronics Association's (NMEA) standard form. In an embodiment, location functionality is incorporated into a specific type of GPS unit, such as a U.S. government issued receiver. The information may be derived from the location presented by the database internal to the device, a database imported into the device, or by the user inputting geo-location parameters of longitude and latitude which may be derived as degrees, minutes and seconds, decimal minutes, or decimal form and translated to the necessary format with the default being ‘decimal’ form. This functionality may be incorporated into a GPS unit. The signal information and the signal classification may then be used to locate the signaling device as well as to provide a direction finding capability.
- A type of triangulation using three units as a group antenna configuration performs direction finding by using multilateration. Commonly used in civil and military surveillance applications, multilateration is able to accurately locate an aircraft, vehicle, or stationary emitter by measuring the “Time Difference of Arrival” (TDOA) of a signal from the emitter at three or more receiver sites. If a pulse is emitted from a platform, it will arrive at slightly different times at two spatially separated receiver sites, the TDOA being due to the different distances of each receiver from the platform. This location information may then be supplied to a mapping process that utilizes a database of mapping images that are extracted from the database based on the latitude and longitude provided by the geo-location or direction finding device. The mapping images may be scanned in to show the points of interest where a signal is either expected to be emanating from based on the database information or from an average taken from the database information and the geo-location calculation performed prior to the mapping software being called. The user can control the map to maximize or minimize the mapping screen to get a better view which is more fit to provide information of the signal transmissions. In an embodiment, the mapping process does not rely on outside mapping software. The mapping capability has the ability to generate the map image and to populate a mapping database that may include information from third party maps to meet specific user requirements.
- In an embodiment, triangulation and multilateration may utilize a Bayesian type filter that may predict possible movement and future location and operation of devices based on input collected from the TDOA and geolocation processes and the variables from the static database pertaining to the specified signal of interest. The Bayesian filter takes the input changes in time difference and its inverse function (i.e., frequency difference) and takes an average changes in signal variation to detect and predict the movement of the signals. The signal changes are measured within 1 ns time difference and the filter may also adapt its gradient error calculation to remove unwanted signals that may cause errors due to signal multipath, inter-symbol interference, and other signal noise.
- In an embodiment the changes within a 1 ns time difference for each sample for each unique signal may be recorded. The spectrum management device may then perform the inverse and compute and record the frequency difference and phase difference between each sample for each unique signal. The spectrum management device may take the same signal and calculates an error based on other input signals coming in within the 1 ns time and may average and filter out the computed error to equalize the signal. The spectrum management device may determine the time difference and frequency difference of arrival for that signal and compute the odds of where the signal is emanating from based on the frequency band parameters presented from the spectral analysis and processor computations, and determines the best position from which the signal is transmitted (i.e., origin of the signal).
-
FIG. 1 illustrates awireless environment 100 suitable for use with the various embodiments. Thewireless environment 100 may includevarious sources mobile devices 104 may generate cellular RF signals 116, such as CDMA, GSM, 3G signals, etc. As another example,wireless access devices 106, such as Wi-Fi® routers, may generateRF signals 118, such as Wi-Fi® signals. As a further example,satellites 108, such as communication satellites or GPS satellites, may generateRF signals 120, such as satellite radio, television, or GPS signals. As a still further example,base stations 110, such as a cellular base station, may generateRF signals 122, such as CDMA, GSM, 3G signals, etc. As another example,radio towers 112, such as local AM or FM radio stations, may generateRF signals 124, such as AM or FM radio signals. As another example, government service provides 114, such as police units, fire fighters, military units, air traffic control towers, etc. may generateRF signals 126, such as radio communications, tracking signals, etc. The various RF signals 116, 118, 120, 122, 124, 126 may be generated at different frequencies, power levels, in different protocols, with different modulations, and at different times. Thevarious sources different sources spectrum management device 102 in thewireless environment 100 may measure the RF energy in thewireless environment 100 across a wide spectrum and identify the different RF signals 116, 118, 120, 122, 124, 126 which may be present in thewireless environment 100. The identification and cataloging of the different RF signals 116, 118, 120, 122, 124, 126 which may be present in thewireless environment 100 may enable thespectrum management device 102 to determine available frequencies for use in thewireless environment 100. In addition, thespectrum management device 102 may be able to determine if there are available frequencies for use in thewireless environment 100 under certain conditions (i.e., day of week, time of day, power level, frequency band, etc.). In this manner, the RF spectrum in thewireless environment 100 may be managed. -
FIG. 2A is a block diagram of aspectrum management device 202 according to an embodiment. Thespectrum management device 202 may include anantenna structure 204 configured to receive RF energy expressed in a wireless environment. Theantenna structure 204 may be any type antenna, and may be configured to optimize the receipt of RF energy across a wide frequency spectrum. Theantenna structure 204 may be connected to one or more optional amplifiers and/orfilters 208 which may boost, smooth, and/or filter the RF energy received byantenna structure 204 before the RF energy is passed to anRF receiver 210 connected to theantenna structure 204. In an embodiment, theRF receiver 210 may be configured to measure the RF energy received from theantenna structure 204 and/or optional amplifiers and/or filters 208. In an embodiment, theRF receiver 210 may be configured to measure RF energy in the time domain and may convert the RF energy measurements to the frequency domain. In an embodiment, theRF receiver 210 may be configured to generate spectral representation data of the received RF energy. TheRF receiver 210 may be any type RF receiver, and may be configured to generate RF energy measurements over a range of frequencies, such as 0 kHz to 24 GHz, 9 kHz to 6 GHz, etc. In an embodiment, the frequency scanned by theRF receiver 210 may be user selectable. In an embodiment, theRF receiver 210 may be connected to asignal processor 214 and may be configured to output RF energy measurements to thesignal processor 214. As an example, theRF receiver 210 may output raw In-Phase (I) and Quadrature (Q) data to thesignal processor 214. As another example, theRF receiver 210 may apply signals processing techniques to output complex In-Phase (I) and Quadrature (Q) data to thesignal processor 214. In an embodiment, the spectrum management device may also include anantenna 206 connected to alocation receiver 212, such as a GPS receiver, which may be connected to thesignal processor 214. Thelocation receiver 212 may provide location inputs to thesignal processor 214. - The
signal processor 214 may include asignal detection module 216, acomparison module 222, atiming module 224, and alocation module 225. Additionally, thesignal processor 214 may include anoptional memory module 226 which may include one or moreoptional buffers 228 for storing data generated by the other modules of thesignal processor 214. - In an embodiment, the
signal detection module 216 may operate to identify signals based on the RF energy measurements received from theRF receiver 210. Thesignal detection module 216 may include a Fast Fourier Transform (FFT)module 217 which may convert the received RF energy measurements into spectral representation data. Thesignal detection module 216 may include ananalysis module 221 which may analyze the spectral representation data to identify one or more signals above a power threshold. Apower module 220 of thesignal detection module 216 may control the power threshold at which signals may be identified. In an embodiment, the power threshold may be a default power setting or may be a user selectable power setting. Anoise module 219 of thesignal detection module 216 may control a signal threshold, such as a noise threshold, at or above which signals may be identified. Thesignal detection module 216 may include aparameter module 218 which may determine one or more signal parameters for any identified signals, such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration, etc. In an embodiment, thesignal processor 214 may include atiming module 224 which may record time information and provide the time information to thesignal detection module 216. Additionally, thesignal processor 214 may include alocation module 225 which may receive location inputs from thelocation receiver 212 and determine a location of thespectrum management device 202. The location of thespectrum management device 202 may be provided to thesignal detection module 216. - In an embodiment, the
signal processor 214 may be connected to one ormore memory 230. Thememory 230 may include multiple databases, such as a history orhistorical database 232 and characteristics listing 236, and one ormore buffers 240 storing data generated bysignal processor 214. While illustrated as connected to thesignal processor 214 thememory 230 may also be on chip memory residing on thesignal processor 214 itself. In an embodiment, the history orhistorical database 232 may include measuredsignal data 234 for signals that have been previously identified by thespectrum management device 202. The measuredsignal data 234 may include the raw RF energy measurements, time stamps, location information, one or more signal parameters for any identified signals, such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration, etc., and identifying information determined from the characteristics listing 236. In an embodiment, the history orhistorical database 232 may be updated as signals are identified by thespectrum management device 202. In an embodiment, thecharacteristic listing 236 may be a database ofstatic signal data 238. Thestatic signal data 238 may include data gathered from various sources including by way of example and not by way of limitation the Federal Communication Commission, the International Telecommunication Union, telecom providers, manufacture data, and data from spectrum management device users.Static signal data 238 may include known signal parameters of transmitting devices, such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration, geographic information for transmitting devices, and any other data that may be useful in identifying a signal. In an embodiment, thestatic signal data 238 and thecharacteristic listing 236 may correlate signal parameters and signal identifications. As an example, thestatic signal data 238 andcharacteristic listing 236 may list the parameters of the local fire and emergency communication channel correlated with a signal identification indicating that signal is the local fire and emergency communication channel. - In an embodiment, the
signal processor 214 may include acomparison module 222 which may match data generated by thesignal detection module 216 with data in the history orhistorical database 232 and/orcharacteristic listing 236. In an embodiment thecomparison module 222 may receive signal parameters from thesignal detection module 216, such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration, and/or receive parameter from thetiming module 224 and/orlocation module 225. Theparameter match module 223 may retrieve data from the history orhistorical database 232 and/or thecharacteristic listing 236 and compare the retrieved data to any received parameters to identify matches. Based on the matches the comparison module may identify the signal. In an embodiment, thesignal processor 214 may be optionally connected to adisplay 242, aninput device 244, and/ornetwork transceiver 246. Thedisplay 242 may be controlled by thesignal processor 214 to output spectral representations of received signals, signal characteristic information, and/or indications of signal identifications on thedisplay 242. In an embodiment, theinput device 244 may be any input device, such as a keyboard and/or knob, mouse, virtual keyboard or even voice recognition, enabling the user of thespectrum management device 202 to input information for use by thesignal processor 214. In an embodiment, thenetwork transceiver 246 may enable thespectrum management device 202 to exchange data with wired and/or wireless networks, such as to update thecharacteristic listing 236 and/or upload information from the history orhistorical database 232. -
FIG. 2B is a schematic logic flow block diagram illustrating logical operations which may be performed by aspectrum management device 202 according to an embodiment. Areceiver 210 may output RF energy measurements, such as I and Q data to aFFT module 252 which may generate a spectral representation of the RF energy measurements which may be output on adisplay 242. The I and Q data may also be buffered in abuffer 256 and sent to asignal detection module 216. Thesignal detection module 216 may receive location inputs from alocation receiver 212 and use the received I and Q data to detect signals. Data from thesignal detection module 216 may be buffered and written into a history orhistorical database 232. Additionally, data from the historical database may be used to aid in the detection of signals by thesignal detection module 216. The signal parameters of the detected signals may be determined by asignal parameters module 218 using information from the history orhistorical database 232 and/or astatic database 238 listing signal characteristics. Data from thesignal parameters module 218 may be stored in the history orhistorical database 232 and/or sent to thesignal detection module 216 and/ordisplay 242. In this manner, signals may be detected and indications of the signal identification may be displayed to a user of the spectrum management device. -
FIG. 3 illustrates a process flow of anembodiment method 300 for identifying a signal. In an embodiment the operations ofmethod 300 may be performed by theprocessor 214 of aspectrum management device 202. Inblock 302 theprocessor 214 may determine the location of thespectrum management device 202. In an embodiment, theprocessor 214 may determine the location of thespectrum management device 202 based on a location input, such as GPS coordinates, received from a location receiver, such as aGPS receiver 212. Inblock 304 theprocessor 214 may determine the time. As an example, the time may be the current clock time as determined by theprocessor 214 and may be a time associated with receiving RF measurements. Inblock 306 theprocessor 214 may receive RF energy measurements. In an embodiment, theprocessor 214 may receive RF energy measurements from anRF receiver 210. Inblock 308 theprocessor 214 may convert the RF energy measurements to spectral representation data. As an example, the processor may apply a Fast Fourier Transform (FFT) to the RF energy measurements to convert them to spectral representation data. Inoptional block 310 theprocessor 214 may display the spectral representation data on adisplay 242 of thespectrum management device 202, such as in a graph illustrating amplitudes across a frequency spectrum. - In
block 312 theprocessor 214 may identify one or more signal above a threshold. In an embodiment, theprocessor 214 may analyze the spectral representation data to identify a signal above a power threshold. A power threshold may be an amplitude measure selected to distinguish RF energies associated with actual signals from noise. In an embodiment, the power threshold may be a default value. In another embodiment, the power threshold may be a user selectable value. Inblock 314 theprocessor 214 may determine signal parameters of any identified signal or signals of interest. As examples, theprocessor 214 may determine signal parameters such as center frequency, bandwidth, power, number of detected signals, frequency peak, peak power, average power, signal duration for the identified signals. Inblock 316 theprocessor 214 may store the signal parameters of each identified signal, a location indication, and time indication for each identified signal in ahistory database 232. In an embodiment, ahistory database 232 may be a database resident in amemory 230 of thespectrum management device 202 which may include data associated with signals actually identified by the spectrum management device. - In
block 318 theprocessor 214 may compare the signal parameters of each identified signal to signal parameters in a signal characteristic listing. In an embodiment, the signal characteristic listing may be astatic database 238 stored in thememory 230 of thespectrum management device 202 which may correlate signal parameters and signal identifications. Indetermination block 320 theprocessor 214 may determine whether the signal parameters of the identified signal or signals match signal parameters in thecharacteristic listing 236. In an embodiment, a match may be determined based on the signal parameters being within a specified tolerance of one another. As an example, a center frequency match may be determined when the center frequencies are within plus or minus 1 kHz of each other. In this manner, differences between real world measured conditions of an identified signal and ideal conditions listed in a characteristics listing may be accounted for in identifying matches. If the signal parameters do not match (i.e., determination block 320=“No”), inblock 326 theprocessor 214 may display an indication that the signal is unidentified on adisplay 242 of thespectrum management device 202. In this manner, the user of the spectrum management device may be notified that a signal is detected, but has not been positively identified. If the signal parameters do match (i.e., determination block 320=“Yes”), inblock 324 theprocessor 214 may display an indication of the signal identification on thedisplay 242. In an embodiment, the signal identification displayed may be the signal identification correlated to the signal parameter in the signal characteristic listing which matched the signal parameter for the identified signal. Upon displaying the indications inblocks processor 214 may return to block 302 and cyclically measure and identify further signals of interest. -
FIG. 4 illustrates anembodiment method 400 for measuring sample blocks of a radio frequency scan. In an embodiment the operations ofmethod 400 may be performed by theprocessor 214 of aspectrum management device 202. As discussed above, inblocks processor 214 may receive RF energy measurements and convert the RF energy measurements to spectral representation data. Inblock 402 theprocessor 214 may determine a frequency range at which to sample the RF spectrum for signals of interest. In an embodiment, a frequency range may be a frequency range of each sample block to be analyzed for potential signals. As an example, the frequency range may be 240 kHz. In an embodiment, the frequency range may be a default value. In another embodiment, the frequency range may be a user selectable value. Inblock 404 theprocessor 214 may determine a number (N) of sample blocks to measure. In an embodiment, each sample block may be sized to the determined of default frequency range, and the number of sample blocks may be determined by dividing the spectrum of the measured RF energy by the frequency range. Inblock 406 theprocessor 214 may assign each sample block a respective frequency range. As an example, if the determined frequency range is 240 kHz, the first sample block may be assigned a frequency range from 0 kHz to 240 kHz, the second sample block may be assigned a frequency range from 240 kHz to 480 kHz, etc. Inblock 408 theprocessor 214 may set the lowest frequency range sample block as the current sample block. Inblock 409 theprocessor 214 may measure the amplitude across the set frequency range for the current sample block. As an example, at each frequency interval (such as 1 Hz) within the frequency range of the sample block theprocessor 214 may measure the received signal amplitude. Inblock 410 theprocessor 214 may store the amplitude measurements and corresponding frequencies for the current sample block. Indetermination block 414 theprocessor 214 may determine if all sample blocks have been measured. If all sample blocks have not been measured (i.e., determination block 414=“No”), inblock 416 theprocessor 214 may set the next highest frequency range sample block as the current sample block. As discussed above, inblocks processor 214 may measure and store amplitudes and determine whether all blocks are sampled. If all blocks have been sampled (i.e., determination block 414=“Yes”), theprocessor 214 may return to block 306 and cyclically measure further sample blocks. -
FIGS. 5A, 5B, and 5C illustrate the process flow for anembodiment method 500 for determining signal parameters. In an embodiment the operations ofmethod 500 may be performed by theprocessor 214 of aspectrum management device 202. Referring toFIG. 5A , inblock 502 theprocessor 214 may receive a noise floor average setting. In an embodiment, the noise floor average setting may be an average noise level for the environment in which thespectrum management device 202 is operating. In an embodiment, the noise floor average setting may be a default setting and/or may be user selectable setting. Inblock 504 theprocessor 214 may receive the signal power threshold setting. In an embodiment, the signal power threshold setting may be an amplitude measure selected to distinguish RF energies associated with actual signals from noise. In an embodiment the signal power threshold may be a default value and/or may be a user selectable setting. Inblock 506 theprocessor 214 may load the next available sample block. In an embodiment, the sample blocks may be assembled according to the operations ofmethod 400 described above with reference toFIG. 4 . In an embodiment, the next available sample block may be an oldest in time sample block which has not been analyzed to determine whether signals of interest are present in the sample block. Inblock 508 theprocessor 214 may average the amplitude measurements in the sample block. Indetermination block 510 theprocessor 214 may determine whether the average for the sample block is greater than or equal to the noise floor average set inblock 502. In this manner, sample blocks including potential signals may be quickly distinguished from sample blocks which may not include potential signals reducing processing time by enabling sample blocks without potential signals to be identified and ignored. If the average for the sample block is lower than the noise floor average (i.e., determination block 510=“No”), no signals of interest may be present in the current sample block. Indetermination block 514 theprocessor 214 may determine whether a cross block flag is set. If the cross block flag is not set (i.e., determination block 514=“No”), inblock 506 theprocessor 214 may load the next available sample block and inblock 508 average thesample block 508. - If the average of the sample block is equal to or greater than the noise floor average (i.e., determination block 510=“Yes”), the sample block may potentially include a signal of interest and in
block 512 theprocessor 214 may reset a measurement counter (C) to 1. The measurement counter value indicating which sample within a sample block is under analysis. Indetermination block 516 theprocessor 214 may determine whether the RF measurement of the next frequency sample (C) is greater than the signal power threshold. In this manner, the value of the measurement counter (C) may be used to control which sample RF measurement in the sample block is compared to the signal power threshold. As an example, when the counter (C) equals 1, the first RF measurement may be checked against the signal power threshold and when the counter (C) equals 2 the second RF measurement in the sample block may be checked, etc. If the C RF measurement is less than or equal to the signal power threshold (i.e., determination block 516=“No”), indetermination block 517 theprocessor 214 may determine whether the cross block flag is set. If the cross block flag is not set (i.e., determination block 517=“No”), indetermination block 522 theprocessor 214 may determine whether the end of the sample block is reached. If the end of the sample block is reached (i.e., determination block 522=“Yes”), inblock 506 theprocessor 214 may load the next available sample block and proceed inblocks block 524 theprocessor 214 may increment the measurement counter (C) so that the next sample in the sample block is analyzed. - If the C RF measurement is greater than the signal power threshold (i.e., determination block 516=“Yes”), in
block 518 theprocessor 214 may check the status of the cross block flag to determine whether the cross block flag is set. If the cross block flag is not set (i.e., determination block 518=“No”), inblock 520 theprocessor 214 may set a sample start. As an example, theprocessor 214 may set a sample start by indicating a potential signal of interest may be discovered in a memory by assigning a memory location for RF measurements associated with the sample start. Referring toFIG. 5B , inblock 526 theprocessor 214 may store the C RF measurement in a memory location for the sample currently under analysis. Inblock 528 theprocessor 214 may increment the measurement counter (C) value. - In
determination block 530 theprocessor 214 may determine whether the C RF measurement (e.g., the next RF measurement because the value of the RF measurement counter was incremented) is greater than the signal power threshold. If the C RF measurement is greater than the signal power threshold (i.e., determination block 530=“Yes”), indetermination block 532 theprocessor 214 may determine whether the end of the sample block is reached. If the end of the sample block is not reached (i.e., determination block 532=“No”), there may be further RF measurements available in the sample block and inblock 526 theprocessor 214 may store the C RF measurement in the memory location for the sample. Inblock 528 the processor may increment the measurement counter (C) and indetermination block 530 determine whether the C RF measurement is above the signal power threshold and inblock 532 determine whether the end of the sample block is reached. In this manner, successive sample RF measurements may be checked against the signal power threshold and stored until the end of the sample block is reached and/or until a sample RF measurement falls below the signal power threshold. If the end of the sample block is reached (i.e., determination block 532=“Yes”), inblock 534 theprocessor 214 may set the cross block flag. In an embodiment, the cross block flag may be a flag in a memory available to theprocessor 214 indicating the signal potential spans across two or more sample blocks. In a further embodiment, prior to setting the cross block flag inblock 534, the slope of a line drawn between the last two RF measurement samples may be used to determine whether the next sample block likely contains further potential signal samples. A negative slope may indicate that the signal of interest is fading and may indicate the last sample was the final sample of the signal of interest. In another embodiment, the slope may not be computed and the next sample block may be analyzed regardless of the slope. - If the end of the sample block is reached (i.e., determination block 532=“Yes”) and in
block 534 the cross block flag is set, referring toFIG. 5A , inblock 506 theprocessor 214 may load the next available sample block, inblock 508 may average the sample block, and inblock 510 determine whether the average of the sample block is greater than or equal to the noise floor average. If the average is equal to or greater than the noise floor average (i.e., determination block 510=“Yes”), inblock 512 theprocessor 214 may reset the measurement counter (C) to 1. Indetermination block 516 theprocessor 214 may determine whether the C RF measurement for the current sample block is greater than the signal power threshold. If the C RF measurement is greater than the signal power threshold (i.e., determination block 516=“Yes”), indetermination block 518 theprocessor 214 may determine whether the cross block flag is set. If the cross block flag is set (i.e., determination block 518=“Yes”), referring toFIG. 5B , inblock 526 theprocessor 214 may store the C RF measurement in the memory location for the sample and inblock 528 the processor may increment the measurement counter (C). As discussed above, inblocks processor 214 may perform operations to determine whether the C RF measurement is greater than the signal power threshold and whether the end of the sample block is reached until the C RF measurement is less than or equal to the signal power threshold (i.e., determination block 530=“No”) or the end of the sample block is reached (i.e., determination block 532=“Yes”). If the end of the sample block is reached (i.e., determination block 532=“Yes”), as discussed above inblock 534 the cross block flag may be set (or verified and remain set if already set) and inblock 535 the C RF measurement may be stored in the sample. - If the end of the sample block is reached (i.e., determination block 532=“Yes”) and in
block 534 the cross block flag is set, referring toFIG. 5A , the processor may perform operations ofblocks FIG. 5B , inblock 538 theprocessor 214 may set the sample stop. As an example, theprocessor 214 may indicate that a sample end is reached in a memory and/or that a sample is complete in a memory. Inblock 540 theprocessor 214 may compute and store complex I and Q data for the stored measurements in the sample. Inblock 542 theprocessor 214 may determine a mean of the complex I and Q data. Referring toFIG. 5C , indetermination block 544 theprocessor 214 may determine whether the mean of the complex I and Q data is greater than a signal threshold. If the mean of the complex I and Q data is less than or equal to the signal threshold (i.e., determination block 544=“No”), inblock 550 theprocessor 214 may indicate the sample is noise and discard data associated with the sample from memory. - If the mean is greater than the signal threshold (i.e., determination block 544=“Yes”), in
block 546 theprocessor 214 may identify the sample as a signal of interest. In an embodiment, theprocessor 214 may identify the sample as a signal of interest by assigning a signal identifier to the signal, such as a signal number or sample number. Inblock 548 theprocessor 214 may determine and store signal parameters for the signal. As an example, theprocessor 214 may determine and store a frequency peak of the identified signal, a peak power of the identified signal, an average power of the identified signal, a signal bandwidth of the identified signal, and/or a signal duration of the identified signal. Inblock 552 theprocessor 214 may clear the cross block flag (or verify that the cross block flag is unset). Inblock 556 theprocessor 214 may determine whether the end of the sample block is reached. If the end of the sample block is not reached (i.e., determination block 556=“No” inblock 558 theprocessor 214 may increment the measurement counter (C), and referring toFIG. 5A indetermination block 516 may determine whether the C RF measurement is greater than the signal power threshold. Referring toFIG. 5C , if the end of the sample block is reached (i.e., determination block 556=“Yes”), referring toFIG. 5A , inblock 506 theprocessor 214 may load the next available sample block. -
FIG. 6 illustrates a process flow for anembodiment method 600 for displaying signal identifications. In an embodiment, the operations ofmethod 600 may be performed by aprocessor 214 of aspectrum management device 202. Indetermination block 602 theprocessor 214 may determine whether a signal is identified. If a signal is not identified (i.e., determination block 602=“No”), inblock 604 theprocessor 214 may wait for the next scan. If a signal is identified (i.e., determination block 602=“Yes”), inblock 606 theprocessor 214 may compare the signal parameters of an identified signal to signal parameters in ahistory database 232. Indetermination block 608 theprocessor 214 may determine whether signal parameters of the identified signal match signal parameters in thehistory database 232. If there is no match (i.e., determination block 608=“No”), inblock 610 theprocessor 214 may store the signal parameters as a new signal in thehistory database 232. If there is a match (i.e., determination block 608=“Yes”), inblock 612 theprocessor 214 may update the matching signal parameters as needed in thehistory database 232. - In
block 614 theprocessor 214 may compare the signal parameters of the identified signal to signal parameters in a signalcharacteristic listing 236. In an embodiment, thecharacteristic listing 236 may be a static database separate from thehistory database 232, and thecharacteristic listing 236 may correlate signal parameters with signal identifications. Indetermination block 616 theprocessor 214 may determine whether the signal parameters of the identified signal match any signal parameters in the signalcharacteristic listing 236. In an embodiment, the match indetermination 616 may be a match based on a tolerance between the signal parameters of the identified signal and the parameters in thecharacteristic listing 236. If there is a match (i.e., determination block 616=“Yes”), inblock 618 theprocessor 214 may indicate a match in thehistory database 232 and inblock 622 may display an indication of the signal identification on adisplay 242. As an example, the indication of the signal identification may be a display of the radio call sign of an identified FM radio station signal. If there is not a match (i.e., determination block 616=“No”), inblock 620 theprocessor 214 may display an indication that the signal is an unidentified signal. In this manner, the user may be notified a signal is present in the environment, but that the signal does not match to a signal in the characteristic listing. -
FIG. 7 illustrates a process flow of anembodiment method 700 for displaying one or more open frequency. In an embodiment, the operations ofmethod 700 may be performed by theprocessor 214 of aspectrum management device 202. Inblock 702 theprocessor 214 may determine a current location of thespectrum management device 202. In an embodiment, theprocessor 214 may determine the current location of thespectrum management device 202 based on location inputs received from alocation receiver 212, such as GPS coordinates received from aGPS receiver 212. Inblock 704 theprocessor 214 may compare the current location to the stored location value in thehistorical database 232. As discussed above, the historical orhistory database 232 may be a database storing information about signals previously actually identified by thespectrum management device 202. Indetermination block 706 theprocessor 214 may determine whether there are any matches between the location information in thehistorical database 232 and the current location. If there are no matches (i.e., determination block 706=“No”), inblock 710 theprocessor 214 may indicate incomplete data is available. In other words the spectrum data for the current location has not previously been recorded. - If there are matches (i.e., determination block 706=“Yes”), in
optional block 708 theprocessor 214 may display a plot of one or more of the signals matching the current location. As an example, theprocessor 214 may compute the average frequency over frequency intervals across a given spectrum and may display a plot of the average frequency over each interval. Inblock 712 theprocessor 214 may determine one or more open frequencies at the current location. As an example, theprocessor 214 may determine one or more open frequencies by determining frequency ranges in which no signals fall or at which the average is below a threshold. Inblock 714 theprocessor 214 may display an indication of one or more open frequency on adisplay 242 of thespectrum management device 202. -
FIG. 8A is a block diagram of aspectrum management device 802 according to an embodiment.Spectrum management device 802 is similar tospectrum management device 202 described above with reference toFIG. 2A , except thatspectrum management device 802 may includesymbol module 816 andprotocol module 806 enabling thespectrum management device 802 to identify the protocol and symbol information associated with an identified signal as well asprotocol match module 814 to match protocol information. Additionally, thecharacteristic listing 236 ofspectrum management device 802 may includeprotocol data 804,environment data 810, andnoise data 812 and anoptimization module 818 may enable thesignal processor 214 to provide signal optimization parameters. - The
protocol module 806 may identify the communication protocol (e.g., LTE, CDMA, etc.) associated with a signal of interest. In an embodiment, theprotocol module 806 may use data retrieved from the characteristic listing, such asprotocol data 804 to help identify the communication protocol. Thesymbol detector module 816 may determine symbol timing information, such as a symbol rate for a signal of interest. Theprotocol module 806 and/orsymbol module 816 may provide data to thecomparison module 222. Thecomparison module 222 may include aprotocol match module 814 which may attempt to match protocol information for a signal of interest toprotocol data 804 in the characteristic listing to identify a signal of interest. Additionally, theprotocol module 806 and/orsymbol module 816 may store data in thememory module 226 and/orhistory database 232. In an embodiment, theprotocol module 806 and/orsymbol module 816 may useprotocol data 804 and/or other data from thecharacteristic listing 236 to help identify protocols and/or symbol information in signals of interest. - The
optimization module 818 may gather information from the characteristic listing, such as noise figure parameters, antenna hardware parameters, and environmental parameters correlated with an identified signal of interest to calculate a degradation value for the identified signal of interest. Theoptimization module 818 may further control thedisplay 242 to output degradation data enabling a user of thespectrum management device 802 to optimize a signal of interest. -
FIG. 8B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to an embodiment. Only those logical operations illustrated inFIG. 8B different from those described above with reference toFIG. 2B will be discussed. As illustrated inFIG. 8B , as received time tracking 850 may be applied to the I and Q data from thereceiver 210. Anadditional buffer 851 may further store the I and Q data received and asymbol detector 852 may identify the symbols of a signal of interest and determine the symbol rate. A multiple accessscheme identifier module 854 may identify whether the signal is part of a multiple access scheme (e.g., CDMA), and aprotocol identifier module 856 may attempt to identify the protocol the signal of interested is associated with. The multiple accessscheme identifier module 854 andprotocol identifier module 856 may retrieve data from thestatic database 238 to aid in the identification of the access scheme and/or protocol. Thesymbol detector module 852 may pass data to the signal parameter and protocol module which may store protocol and symbol information in addition to signal parameter information for signals of interest. -
FIG. 9 illustrates a process flow of anembodiment method 900 for determining protocol data and symbol timing data. In an embodiment, the operations ofmethod 900 may be performed by theprocessor 214 of aspectrum management device 802. Indetermination block 902 theprocessor 214 may determine whether two or more signals are detected. If two or more signals are not detected (i.e., determination block 902=“No”), indetermination block 902 theprocessor 214 may continue to determine whether two or more signals are detected. If two or more signals are detected (i.e., determination block 902=“Yes”), indetermination block 904 theprocessor 214 may determine whether the two or more signals are interrelated. In an embodiment, a mean correlation value of the spectral decomposition of each signal may indicate the two or more signals are interrelated. As an example, a mean correlation of each signal may generate a value between 0.0 and 1, and theprocessor 214 may compare the mean correlation value to a threshold, such as a threshold of 0.75. In such an example, a mean correlation value at or above the threshold may indicate the signals are interrelated while a mean correlation value below the threshold may indicate the signals are not interrelated and may be different signals. In an embodiment, the mean correlation value may be generated by running a full energy bandwidth correlation of each signal, measuring the values of signal transition for each signal, and for each signal transition running a spectral correlation between signals to generate the mean correlation value. If the signals are not interrelated (i.e., determination block 904=“No”), the signals may be two or more different signals, and inblock 907processor 214 may measure the interference between the two or more signals. In an optional embodiment, inoptional block 909 theprocessor 214 may generate a conflict alarm indicating the two or more different signals interfere. In an embodiment, the conflict alarm may be sent to the history database and/or a display. Indetermination block 902 theprocessor 214 may continue to determine whether two or more signals are detected. If the two signal are interrelated (i.e., determination block 904=“Yes”), inblock 905 theprocessor 214 may identify the two or more signals as a single signal. Inblock 906 theprocessor 214 may combine signal data for the two or more signals into a signal single entry in the history database. Indetermination block 908 theprocessor 214 may determine whether the signals mean averages. If the mean averages (i.e., determination block 908=“Yes”), theprocessor 214 may identify the signal as havingmultiple channels 910. If the mean does not average (i.e., determination block 908=“Yes”) or after identifying the signal as havingmultiple channels 910, inblock 914 theprocessor 214 may determine and store protocol data for the signal. Inblock 916 theprocessor 214 may determine and store symbol timing data for the signal, and themethod 900 may return to block 902. -
FIG. 10 illustrates a process flow of anembodiment method 1000 for calculating signal degradation data. In an embodiment, the operations ofmethod 1000 may be performed by theprocessor 214 of aspectrum management device 202. Inblock 1002 the processor may detect a signal. Inblock 1004 theprocessor 214 may match the signal to a signal in a static database. Inblock 1006 theprocessor 214 may determine noise figure parameters based on data in thestatic database 236 associated with the signal. As an example, theprocessor 214 may determine the noise figure of the signal based on parameters of a transmitter outputting the signal according to thestatic database 236. Inblock 1008 theprocessor 214 may determine hardware parameters associated with the signal in thestatic database 236. As an example, theprocessor 214 may determine hardware parameters such as antenna position, power settings, antenna type, orientation, azimuth, location, gain, and equivalent isotropically radiated power (EIRP) for the transmitter associated with the signal from thestatic database 236. Inblock 1010processor 214 may determine environment parameters associated with the signal in thestatic database 236. As an example, theprocessor 214 may determine environment parameters such as rain, fog, and/or haze based on a delta correction factor table stored in the static database and a provided precipitation rate (e.g., mm/hr). Inblock 1012 theprocessor 214 may calculate and store signal degradation data for the detected signal based at least in part on the noise figure parameters, hardware parameters, and environmental parameters. As an example, based on the noise figure parameters, hardware parameters, and environmental parameters free space losses of the signal may be determined. Inblock 1014 theprocessor 214 may display the degradation data on adisplay 242 of thespectrum management device 202. In a further embodiment, the degradation data may be used with measured terrain data of geographic locations stored in the static database to perform pattern distortion, generate propagation and/or next neighbor interference models, determine interference variables, and perform best fit modeling to aide in signal and/or system optimization. -
FIG. 11 illustrates a process flow of anembodiment method 1100 for displaying signal and protocol identification information. In an embodiment, the operations ofmethod 1100 may be performed by aprocessor 214 of aspectrum management device 202. Inblock 1102 theprocessor 214 may compare the signal parameters and protocol data of an identified signal to signal parameters and protocol data in ahistory database 232. In an embodiment, ahistory database 232 may be a database storing signal parameters and protocol data for previously identified signals. Inblock 1104 theprocessor 214 may determine whether there is a match between the signal parameters and protocol data of the identified signal and the signal parameters and protocol data in thehistory database 232. If there is not a match (i.e.,determination block 1104=“No”), inblock 1106 theprocessor 214 may store the signal parameters and protocol data as a new signal in thehistory database 232. If there is a match (i.e.,determination block 1104=“Yes”), inblock 1108 theprocessor 214 may update the matching signal parameters and protocol data as needed in thehistory database 232. - In
block 1110 theprocessor 214 may compare the signal parameters and protocol data of the identified signal to signal parameters and protocol data in the signalcharacteristic listing 236. Indetermination block 1112 theprocessor 214 may determine whether the signal parameters and protocol data of the identified signal match any signal parameters and protocol data in the signalcharacteristic listing 236. If there is a match (i.e.,determination block 1112=“Yes”), inblock 1114 theprocessor 214 may indicate a match in the history database and inblock 1118 may display an indication of the signal identification and protocol on a display. If there is not a match (i.e.,determination block 1112=“No”), inblock 1116 theprocessor 214 may display an indication that the signal is an unidentified signal. In this manner, the user may be notified a signal is present in the environment, but that the signal does not match to a signal in the characteristic listing. -
FIG. 12A is a block diagram of aspectrum management device 1202 according to an embodiment.Spectrum management device 1202 is similar tospectrum management device 802 described above with reference toFIG. 8A , except thatspectrum management device 1202 may include TDOA/FDOA module 1204 andmodulation module 1206 enabling thespectrum management device 1202 to identify the modulation type employed by a signal of interest and calculate signal origins. Themodulation module 1206 may enable the signal processor to determine the modulation applied to signal, such as frequency modulation (e.g., FSK, MSK, etc.) or phase modulation (e.g., BPSK, QPSK, QAM, etc.) as well as to demodulate the signal to identify payload data carried in the signal. Themodulation module 1206 may usepayload data 1221 from the characteristic listing to identify the data types carried in a signal. As examples, upon demodulating a portion of the signal the payload data may enable theprocessor 214 to determine whether voice data, video data, and/or text based data is present in the signal. The TDOA/FDOA module 1204 may enable thesignal processor 214 to determine time difference of arrival for signals or interest and/or frequency difference of arrival for signals of interest. Using the TDOA/FDOA information estimates of the origin of a signal may be made and passed to a mapping module 1225 which may control thedisplay 242 to output estimates of a position and/or direction of movement of a signal. -
FIG. 12B is a schematic logic flow block diagram illustrating logical operations which may be performed by a spectrum management device according to an embodiment. Only those logical operations illustrated inFIG. 12B different from those described above with reference toFIG. 8B will be discussed. Time tracking 850 may additionally include TDOA and/or FDOA (see 1250). A magnitude squared 1252 operation may be performed on data from thesymbol detector 852 to identify whether frequency or phase modulation is present in the signal. Phase modulated signals may be identified by thephase modulation 1254 processes and frequency modulated signals may be identified by the frequency modulation processes 1256. The modulation information may be passed to a signal parameters, protocols, andmodulation module 1258. -
FIG. 13 illustrates a process flow of anembodiment method 1300 for estimating a signal origin based on a frequency difference of arrival. In an embodiment, the operations ofmethod 1300 may be performed by aprocessor 214 of aspectrum management device 1202. Inblock 1302 theprocessor 214 may compute frequency arrivals and phase arrivals for multiple instances of an identified signal. Inblock 1304 theprocessor 214 may determine frequency difference of arrival for the identified signal based on the computed frequency difference and phase difference. Inblock 1306 the processor may compare the determined frequency difference of arrival for the identified signal to data associated with known emitters in the characteristic listing to estimate an identified signal origin. Inblock 1308 theprocessor 214 may indicate the estimated identified signal origin on a display of the spectrum management device. As an example, theprocessor 214 may overlay the estimated origin on a map displayed by the spectrum management device. -
FIG. 14 illustrates a process flow of an embodiment method for displaying an indication of an identified data type within a signal. In an embodiment, the operations ofmethod 1400 may be performed by aprocessor 214 of aspectrum management device 1202. Inblock 1402 theprocessor 214 may determine the signal parameters for an identified signal of interest. Inblock 1404 theprocessor 214 may determine the modulation type for the signal of interest. Inblock 1406 theprocessor 214 may determine the protocol data for the signal of interest. Inblock 1408 theprocessor 214 may determine the symbol timing for the signal of interest. Inblock 1410 theprocessor 214 may select a payload scheme based on the determined signal parameters, modulation type, protocol data, and symbol timing. As an example, the payload scheme may indicate how data is transported in a signal. For example, data in over the air television broadcasts may be transported differently than data in cellular communications and the signal parameters, modulation type, protocol data, and symbol timing may identify the applicable payload scheme to apply to the signal. Inblock 1412 theprocessor 214 may apply the selected payload scheme to identify the data type or types within the signal of interest. In this manner, theprocessor 214 may determine what type of data is being transported in the signal, such as voice data, video data, and/or text based data. Inblock 1414 the processor may store the data type or types. Inblock 1416 theprocessor 214 may display an indication of the identified data types. -
FIG. 15 illustrates a process flow of anembodiment method 1500 for determining modulation type, protocol data, and symbol timing data.Method 1500 is similar tomethod 900 described above with reference toFIG. 9 , except that modulation type may also be determined. In an embodiment, the operations ofmethod 1500 may be performed by aprocessor 214 of aspectrum management device 1202. In blocks 902, 904, 905, 906, 908, and 910 theprocessor 214 may perform operations of like numbered blocks ofmethod 900 described above with reference toFIG. 9 . Inblock 1502 the processor may determine and store a modulation type. As an example, a modulation type may be an indication that the signal is frequency modulated (e.g., FSK, MSK, etc.) or phase modulated (e.g., BPSK, QPSK, QAM, etc.). As discussed above, inblock 914 the processor may determine and store protocol data and inblock 916 the processor may determine and store timing data. - In an embodiment, based on signal detection, a time tracking module, such as a TDOA/
FDOA module 1204, may track the frequency repetition interval at which the signal is changing. The frequency repetition interval may also be tracked for a burst signal. In an embodiment, the spectrum management device may measure the signal environment and set anchors based on information stored in the historic or static database about known transmitter sources and locations. In an embodiment, the phase information about a signal be extracted using a spectral decomposition correlation equation to measure the angle of arrival (“AOA”) of the signal. In an embodiment, the processor of the spectrum management device may determine the received power as the Received Signal Strength (“RSS”) and based on the AOA and RSS may measure the frequency difference of arrival. In an embodiment, the frequency shift of the received signal may be measured and aggregated over time. In an embodiment, after an initial sample of a signal, known transmitted signals may be measured and compared to the RSS to determine frequency shift error. In an embodiment, the processor of the spectrum management device may compute a cross ambiguity function of aggregated changes in arrival time and frequency of arrival. In an additional embodiment, the processor of the spectrum management device may retrieve FFT data for a measured signal and aggregate the data to determine changes in time of arrival and frequency of arrival. In an embodiment, the signal components of change in frequency of arrival may be averaged through a Kalman filter with a weighted tap filter from 2 to 256 weights to remove measurement error such as noise, multipath interference, etc. In an embodiment, frequency difference of arrival techniques may be applied when either the emitter of the signal or the spectrum management device are moving or when then emitter of the signal and the spectrum management device are both stationary. When the emitter of the signal and the spectrum management device are both stationary the determination of the position of the emitter may be made when at least four known other known signal emitters positions are known and signal characteristics may be available. In an embodiment, a user may provide the four other known emitters and/or may use already in place known emitters, and may use the frequency, bandwidth, power, and distance values of the known emitters and their respective signals. In an embodiment, where the emitter of the signal or spectrum management device may be moving, frequency deference of arrival techniques may be performed using two known emitters. -
FIG. 16 illustrates an embodiment method for tracking a signal origin. In an embodiment, the operations ofmethod 1600 may be performed by aprocessor 214 of aspectrum management device 1202. Inblock 1602 theprocessor 214 may determine a time difference of arrival for a signal of interest. Inblock 1604 theprocessor 214 may determine a frequency difference of arrival for the signal interest. As an example, theprocessor 214 may take the inverse of the time difference of arrival to determine the frequency difference of arrival of the signal of interest. Inblock 1606 theprocessor 214 may identify the location. As an example, theprocessor 214 may determine the location based on coordinates provided from a GPS receiver. Indetermination block 1608 theprocessor 214 may determine whether there are at least four known emitters present in the identified location. As an example, theprocessor 214 may compare the geographic coordinates for the identified location to a static database and/or historical database to determine whether at least four known signals are within an area associated with the geographic coordinates. If at least four known emitters are present (i.e.,determination block 1608=“Yes”), inblock 1612 theprocessor 214 may collect and measure the RSS of the known emitters and the signal of interest. As an example, theprocessor 214 may use the frequency, bandwidth, power, and distance values of the known emitters and their respective signals and the signal of interest. If less than four known emitters are present (i.e.,determination block 1608=“No”), inblock 1610 theprocessor 214 may measure the angle of arrival for the signal of interest and the known emitter. Using the RSS or angle or arrival, inblock 1614 theprocessor 214 may measure the frequency shift and inblock 1616 theprocessor 214 may obtain the cross ambiguity function. Indetermination block 1618 theprocessor 214 may determine whether the cross ambiguity function converges to a solution. If the cross ambiguity function does converge to a solution (i.e.,determination block 1618=“Yes”), inblock 1620 theprocessor 214 may aggregate the frequency shift data. Inblock 1622 theprocessor 214 may apply one or more filter to the aggregated data, such as a Kalman filter. Additionally, theprocessor 214 may apply equations, such as weighted least squares equations and maximum likelihood equations, and additional filters, such as a non-line-of-sight (“NLOS”) filters to the aggregated data. In an embodiment, the cross ambiguity function may resolve the position of the emitter of the signal of interest to within 3 meters. If the cross ambiguity function does not converge to a solution (i.e.,determination block 1618=“No”), inblock 1624 theprocessor 214 may determine the time difference of arrival for the signal and inblock 1626 theprocessor 214 may aggregate the time shift data. Additionally, the processor may filter the data to reduce interference. Whether based on frequency difference of arrival or time difference of arrival, the aggregated and filtered data may indicate a position of the emitter of the signal of interest, and in block 1628 theprocessor 214 may output the tracking information for the position of the emitter of the signal of interest to a display of the spectrum management device and/or the historical database. In an additional embodiment, location of emitters, time and duration of transmission at a location may be stored in the history database such that historical information may be used to perform and predict movement of signal transmission. In a further embodiment, the environmental factors may be considered to further reduce the measured error and generate a more accurate measurement of the location of the emitter of the signal of interest. - The
processor 214 ofspectrum management devices internal memory processor 214. Theprocessor 214 may include internal memory sufficient to store the application software instructions. In many devices the internal memory may be a volatile or nonvolatile memory, such as flash memory, or a mixture of both. For the purposes of this description, a general reference to memory refers to memory accessible by theprocessor 214 including internal memory or removable memory plugged into the device and memory within theprocessor 214 itself. - Identifying Devices in White Space.
- The present invention provides for systems, methods, and apparatus solutions for device sensing in white space, which improves upon the prior art by identifying sources of signal emission by automatically detecting signals and creating unique signal profiles. Device sensing has an important function and applications in military and other intelligence sectors, where identifying the emitter device is crucial for monitoring and surveillance, including specific emitter identification (SEI).
- At least two key functions are provided by the present invention: signal isolation and device sensing. Signal Isolation according to the present invention is a process whereby a signal is detected, isolated through filtering and amplification, amongst other methods, and key characteristics extracted. Device Sensing according to the present invention is a process whereby the detected signals are matched to a device through comparison to device signal profiles and may include applying a confidence level and/or rating to the signal-profile matching. Further, device sensing covers technologies that permit storage of profile comparisons such that future matching can be done with increased efficiency and/or accuracy. The present invention systems, methods, and apparatus are constructed and configured functionally to identify any signal emitting device, including by way of example and not limitation, a radio, a cell phone, etc.
- Regarding signal isolation, the following functions are included in the present invention: amplifying, filtering, detecting signals through energy detection, waveform-based, spectral correlation-based, radio identification-based, or matched filter method, identifying interference, identifying environmental baseline(s), and/or identify signal characteristics.
- Regarding device sensing, the following functions are included in the present invention: using signal profiling and/or comparison with known database(s) and previously recorded profile(s), identifying the expected device or emitter, stating the level of confidence for the identification, and/or storing profiling and sensing information for improved algorithms and matching. In preferred embodiments of the present invention, the identification of the at least one signal emitting device is accurate to a predetermined degree of confidence between about 80 and about 95 percent, and more preferably between about 80 and about 100 percent. The confidence level or degree of confidence is based upon the amount of matching measured data compared with historical data and/or reference data for predetermined frequency and other characteristics.
- The present invention provides for wireless signal-emitting device sensing in the white space based upon a measured signal, and considers the basis of license(s) provided in at least one reference database, preferably the federal communication commission (FCC) and/or other defined database including license listings. The methods include the steps of providing a device for measuring characteristics of signals from signal emitting devices in a spectrum associated with wireless communications, the characteristics of the measured data from the signal emitting devices including frequency, power, bandwidth, duration, modulation, and combinations thereof; making an assessment or categorization on analog and/or digital signal(s); determining the best fit based on frequency if the measured power spectrum is designated in historical and/or reference data, including but not limited to the FCC or other database(s) for select frequency ranges; determining analog or digital, based on power and sideband combined with frequency allocation; determining a TDM/FDM/CDM signal, based on duration and bandwidth; determining best modulation fit for the desired signal, if the bandwidth and duration match the signal database(s); adding modulation identification to the database; listing possible modulations with best percentage fit, based on the power, bandwidth, frequency, duration, database allocation, and combinations thereof; and identifying at least one signal emitting device from the composite results of the foregoing steps. Additionally, the present invention provides that the phase measurement of the signal is calculated between the difference of the end frequency of the bandwidth and the peak center frequency and the start frequency of the bandwidth and the peak center frequency to get a better measurement of the sideband drop off rate of the signal to help determine the modulation of the signal.
- In embodiments of the present invention, an apparatus is provided for automatically identifying devices in a spectrum, the apparatus including a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in a spectrum associated with wireless communications; and wherein the apparatus is operable to automatically analyze the measured data to identify at least one signal emitting device in near real time from attempted detection and identification of the at least one signal emitting device. The characteristics of signals and measured data from the signal emitting devices include frequency, power, bandwidth, duration, modulation, and combinations thereof.
- The present invention systems including at least one apparatus, wherein the at least one apparatus is operable for network-based communication with at least one server computer including a database, and/or with at least one other apparatus, but does not require a connection to the at least one server computer to be operable for identifying signal emitting devices; wherein each of the apparatus is operable for identifying signal emitting devices including: a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in a spectrum associated with wireless communications; and wherein the apparatus is operable to automatically analyze the measured data to identify at least one signal emitting device in near real time from attempted detection and identification of the at least one signal emitting device.
- Identifying Open Space in a Wireless Communication Spectrum.
- The present invention provides for systems, methods, and apparatus solutions for automatically identifying open space, including open space in the white space of a wireless communication spectrum. Importantly, the present invention identifies the open space as the space that is unused and/or seldomly used (and identifies the owner of the licenses for the seldomly used space, if applicable), including unlicensed spectrum, white space, guard bands, and combinations thereof. Method steps of the present invention include: automatically obtaining a listing or report of all frequencies in the frequency range; plotting a line and/or graph chart showing power and bandwidth activity; setting frequencies based on a frequency step and/or resolution so that only user-defined frequencies are plotted; generating files, such as by way of example and not limitation, .csv or .pdf files, showing average and/or aggregated values of power, bandwidth and frequency for each derived frequency step; and showing an activity report over time, over day vs. night, over frequency bands if more than one, in white space if requested, in Industrial, Scientific, and Medical (ISM) band or space if requested; and if frequency space is seldomly in that area, then identify and list frequencies and license holders.
- Additional steps include: automatically scanning the frequency span, wherein a default scan includes a frequency span between about 54 MHz and about 804 MHz; an ISM scan between about 900 MHz and about 2.5 GHz; an ISM scan between about 5 GHz and about 5.8 GHz; and/or a frequency range based upon inputs provided by a user. Also, method steps include scanning for an allotted amount of time between a minimum of about 15 minutes up to about 30 days; preferably scanning for allotted times selected from the following: a minimum of about 15 minutes; about 30 minutes; about 1 hour increments; about 5 hour increments; about 10 hour increments; about 24 hours; about 1 day; and about up to 30 days; and combinations thereof. In preferred embodiments, if the apparatus is configured for automatically scanning for more than about 15 minutes, then the apparatus is preferably set for updating results, including updating graphs and/or reports for an approximately equal amount of time (e.g., every 15 minutes).
- The systems, methods, and apparatus also provide for automatically calculating a percent activity associated with the identified open space on predetermined frequencies and/or ISM bands.
- Signal Database.
- Preferred embodiments of the present invention provide for sensed and/or measured data received by the at least one apparatus of the present invention, analyzed data, historical data, and/or reference data, change-in-state data, and any updates thereto, are storable on each of the at least one apparatus. In systems of the present invention, each apparatus further includes transmitters for sending the sensed and/or measured data received by the at least one apparatus of the present invention, analyzed data, historical data, and/or reference data, change-in-state data, and any updates thereto, are communicated via the network to the at least one remote server computer and its corresponding database(s). Preferably, the server(s) aggregate the data received from the multiplicity of apparatus or devices to produce a composite database for each of the types of data indicated. Thus, while each of the apparatus or devices is fully functional and self-contained within the housing for performing all method steps and operations without network-based communication connectivity with the remote server(s), when connected, as illustrated in
FIG. 28 , the distributed devices provide the composite database, which allows for additional analytics not possible for individual, isolated apparatus or device units (when not connected in network-based communication), which solves a longstanding, unmet need. - In particular, the aggregation of data from distributed, different apparatus or device units allow for comparison of sample sets of data to compare signal data or information for similar factors, including time(s), day(s), venues, geographic locations or regions, situations, activities, etc., as well as for comparing various signal characteristics with the factors, wherein the signal characteristics and their corresponding sensed and/or measured data, including raw data and change-in-state data, and/or analyzed data from the signal emitting devices include frequency, power, bandwidth, duration, modulation, and combinations thereof. Preferably, the comparisons are conducted in near real time. The aggregation of data may provide for information about the same or similar mode from apparatus to apparatus, scanning the same or different frequency ranges, with different factors and/or signal characteristics received and stored in the database(s), both on each apparatus or device unit, and when they are connected in network-based communication for transmission of the data to the at least one remote server.
- The aggregation of data from a multiplicity of units also advantageously provide for continuous, 24 hours/7 days per week scanning, and allows the system to identify sections that exist as well as possibly omitted information or lost data, which may still be considered for comparisons, even if it is incomplete. From a time standpoint, there may not be a linearity with respect to when data is collected or received by the units; rather, the systems and methods of the present invention provide for automated matching of time, i.e., matching timeframes and relative times, even where the environment, activities, and/or context may be different for different units. By way of example and not limitation, different units may sense and/or measure the same signal from the same signal emitting device in the spectrum, but interference, power, environmental factors, and other factors may present identification issues that preclude one of the at last one apparatus or device units from determining the identity of the signal emitting device with the same degree of certainty or confidence. The variation in this data from a multiplicity of units measuring the same signals provides for aggregation and comparison at the remote server using the distributed databases from each unit to generate a variance report in near real time. The variance data utilizes value changes or deltas, in the signals rather than complete representations of the signals, either analog or digital, to represent how a signal changes, which advantageously reduces processing times for analysis and for report generation, which provides for near real time generation of the reports, preferably in less than about 5 minutes, including physical printout and/or visual display on GUI; the variance reports and variance data include correlation between signal deltas and database deltas to identify and categorize a signal, and also include comparison of spectrum variance to determine spectrum activities for a period of time. Variance reports may also include data from more than one of the apparatus units to compare differences or identify variations between them for the same time and same signal targets.
- The database(s) further provide repository database in memory on the apparatus or device units, and/or data from a multiplicity of units are aggregated on at least one remote server to provide an active network with distributed nodes over a region that produce an active or dynamic database of signals, identified devices, identified open space, and combinations thereof, and the nodes may report to or transmit data via network-based communication to a central hub or server. This provides for automatically comparing signal emitting devices or their profiles and corresponding sensed or measured data, situations, activities, geographies, times, days, and/or environments, which provides unique composite and comparison data that may be continuously updated, and includes in the near real time reports automatically generated at predetermined times, at user-specified times, on-demand, and/or when data changes occur beyond an expected range. Other reports data may include sample size, power usage, average power levels, and interference.
- Overall, the significant benefits provided by the present invention automatically generated reports in near real time is that the RF environment may be readily analyzed and communicated using real time or near real time data, so that the reports information is actionable to make changes to improve or optimize signals or to modify the environment for the signals and their corresponding devices. This solves a longstanding unmet need from the prior art.
-
FIG. 29 shows a schematic diagram illustrating aspects of the systems, methods and apparatus according to the present invention. Each node includes an apparatus or device unit, referenced in theFIG. 1 as “SigSet Device A”, “SigSet Device B”, “SigSet Device C”, and through “SigSet Device N” that are constructed and configured for selective exchange, both transmitting and receiving information over a network connection, either wired or wireless communications, with the master SigDB or database at a remote server location from the units. - Furthermore, the database aggregating nodes of the apparatus or device units provide a baseline compared with new data, which provide for near real time analysis and results within each of the at least one apparatus or device unit, which calculates and generates results such as signal emitting device identification, identification of open space, signal optimization, and combinations thereof, based upon the particular settings of each of the at least one apparatus or device unit. The settings include frequency ranges, location and distance from other units, difference in propagation from one unit to another unit, and combinations thereof, which factor into the final results.
- The present invention systems, methods, and apparatus embodiments provide for leveraging the use of deltas or differentials from the baseline, as well as actual data, to provide onsite sensing, measurement, and analysis for a given environment and spectrum, for each of the at least one apparatus or device unit. Because the present invention provides the at least one processor on each unit to compare signals and signal characteristic differences using compressed data for deltas to provide near real time results, the database storage may further be optimized by storing compressed data and/or deltas, and then decompressing and/or reconstructing the actual signals using the deltas and the baseline. Analytics are also provided using this approach. So then the signals database(s) provide for reduced data storage to the smallest sample set that still provides at least the baseline and the deltas to enable signal reconstruction and analysis to produce the results described according to the present invention.
- Preferably, the modeling and virtualization analytics enabled by the databases on each of the at least one apparatus or device units independently of the remote server computer, and also provided on the remote server computer from aggregated data, provide for “gap filling” for omitted or absent data, and or for reconstruction from deltas. A multiplicity of deltas may provide for signal identification, interference identification, neighboring band identification, device identification, signal optimization, and combinations, all in near real time. Significantly, the deltas approach of the present invention which provide for minimization of data sets or sample data sets required for comparisons and/or analytics, i.e., the smallest range of time, frequency, etc. that captures all representative signals and/or deltas associated with the signals, environment conditions, noise, etc.
- The signal database(s) may be represented with visual indications including diagrams, graphs, plots, tables, and combinations thereof, which may be presented directly by the apparatus or device unit to its corresponding display contained within the housing. Also, the signals database(s) provide each apparatus or device unit to receive a first sample data set in a first time period, and receive a second sample data set in a second time period, and receive a N sample data set in a corresponding N time period; to save or store each of the at least two distinct sample data sets; to automatically compare the at least two sample data sets to determine a change-in-state or “delta”. Preferably, the database receives and stores at least the first of the at least two data sets and also stores the delta. The stored delta values provide for quick analytics and regeneration of the actual values of the sample sets from the delta values, which advantageously contributes to the near real time results of the present invention.
- In preferred embodiments of the present invention, the at least one apparatus is continuously scanning the environment for signals, deltas from prior at least one sample data set, and combinations, which are categorized, classified, and stored in memory.
- In preferred embodiments of the present invention, the at least one apparatus is continuously scanning the environment for signals, deltas from prior at least one sample data set, and combinations, which are categorized, classified, and stored in memory.
- The systems, methods and apparatus embodiments of the present invention include hardware and software components and requirements to provide for each of the apparatus units to connect and communicate different data they sense, measure, analyze, and/or store on local database(s) in memory on each of the units with the remote server computer and database. Thus the master database or “SigDB” is operable to be applied and connect to the units, and may include hardware and software commercially available, for example SQL Server 2012, and to be applied to provide a user the criteria to upgrade/update their current sever network to the correct configuration that is required to operate and access the SigDB. Also, the SigDB is preferably designed, constructed and as a full hardware and software system configuration for the user, including load testing and network security and configuration. Other exemplary requirements include that the SigDB will include a database structure that can sustain a multiplicity of apparatus units' information; provide a method to update the FCC database and/or historical database according a set time (every month/quarter/week, etc.), and in accordance with changes to the FCC.gov databases that are integrated into the database; operable to receive and to download unit data from a remote location through a network connection; be operable to query apparatus unit data stored within the SigDB database server and to query apparatus unit data in ‘present’ time to a particular apparatus unit device for a given ‘present’ time not available in the current SigDB server database; update this information into its own database structure; to keep track of Device Identifications and the information each apparatus unit is collecting including its location; to query the apparatus units based on Device ID or location of device or apparatus unit; to connect to several devices and/or apparatus units on a distributed communications network; to partition data from each apparatus unit or device and differentiate the data from each based on its location and Device ID; to join queries from several devices if a user wants to know information acquired from several remote apparatus units at a given time; to provide ability for several users (currently up to 5 per apparatus unit or device) to query information from the SigDB database or apparatus unit or device; to grant access permissions to records for each user based on device ID, pertinent information or tables/location; to connect to a user GUI from a remote device such as a workstation or tablet PC from a Web App application; to retrieve data queries based on user information and/or jobs; to integrate database external database information from the apparatus units; and combinations thereof.
- Also, in preferred embodiments, a GUI interface based on a Web Application software is provided; in one embodiment, the SigDB GUI is provided in any appropriate software, such as by way of example, in Visual Studio using .Net/Asp.Net technology or JavaScript. In any case, the SigDB GUI preferably operates across cross platform systems with correct browser and operating system (OS) configuration; provides the initial requirements of a History screen in each apparatus unit to access sever information or query a remote apparatus unit containing the desired user information; and, generates .csv and .pdf reports that are useful to the user.
- Automated Reports and Visualization of Analytics.
- Various reports for describing and illustrating with visualization the data and analysis of the device, system and method results from spectrum management activities include at least reports on power usage, RF survey, and/or variance, as well as interference detection, intermodulation detection, uncorrelated licenses, and/or open space identification.
- The systems, methods, and devices of the various embodiments enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements. In an embodiment, signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user. In another embodiment, the protocols of signals may also be identified. In a further embodiment, the modulation of signals, devices or device types emitting signals, data types carried by the signals, and estimated signal origins may be identified, and resulting information provided in automatically generated reports.
- Reporting features of the present invention preferably include and support all of the sensing, measurements, analytics, and/or data for each of the at least one apparatus units in systems and methods, including SigDB databases and its advanced analytics. By way of example and not limitation, the reporting features include: frequency, power, bandwidth, time, and combinations thereof. In one embodiment of the present invention, the reports are selected from the group consisting essentially of: variance reports, power usage reports, RF survey reports, signal optimization reports, and combinations thereof. Variance reports provide information about the changes in spectrum usage between time periods, between locations, and/or between changes in state. Power usage reports provide information about power variables, including but not limited to amplitude, bandwidth, and time, for one or more frequency channels within the spectrum. RF survey reports provide detailed information about the spectrum usage and interference for particular signals and/or sites or locations. Signal optimization reports include information about interference and options for actions to take to optimize the signal(s) of focus.
- Variance reports provide information on variations within the spectrum. In one example of a report and methods for generating it, consider finding Open Space based upon frequency range and time; the at least one apparatus unit of the system is operable to automatically generate the report following the steps of: after sensing, measuring and/or analyzing the data, group all frequencies by at least one specific frequency range of the measured value collected; automatically check frequencies, and if more than one of the same frequency exists then use the highest and lowest frequency in the group and generate an average frequency, use the highest and lowest power in the group and generate an average power, use highest and lowest bandwidth in the group and generate average bandwidth; group frequencies in order of least to greatest (e.g., ascending order); automatically generate a diagram of Plot Line Graph of Frequency (x-axis) vs Power (y-axis) use FreqAvg and PwrAvg; where multiple same values exist, then automatically apply a smoothing filter and average the graph; set timer and average over time; take a new scan of frequencies and add additional new frequencies that have appeared; average existing same frequencies and update graph; and repeat after each time.
- In preferred embodiments of the present invention, the at least one apparatus is continuously scanning the environment for signals, deltas from prior at least one sample data set, and combinations, which are categorized, classified, and stored in memory, which are used in automatically generating reports at predetermined times, when specified by a user, and/or at times when updates or deltas are detected or determined. Any and all data, including deltas data, sample data and corresponding sample size, are preferably selectively available for inclusion in the automatically generated reports for near real time data reporting.
- Referring again to the drawings,
FIG. 17 is a schematic diagram illustrating an embodiment for scanning and finding open space. A plurality of nodes are in wireless or wired communication with a software defined radio, which receives information concerning open channels following real-time scanning and access to external database frequency information. -
FIG. 18 is a diagram of an embodiment of the invention wherein software defined radio nodes are in wireless or wired communication with a master transmitter and device sensing master. -
FIG. 19 is a process flow diagram of an embodiment method of temporally dividing up data into intervals for power usage analysis and comparison. The data intervals are initially set to seconds, minutes, hours, days and weeks, but can be adjusted to account for varying time periods (e.g., if an overall interval of data is only a week, the data interval divisions would not be weeks). In one embodiment, the interval slicing of data is used to produce power variance information and reports. -
FIG. 20 is a flow diagram illustrating an embodiment wherein frequency to license matching occurs. In such an embodiment the center frequency and bandwidth criteria can be checked against a database to check for a license match. Both licensed and unlicensed bands can be checked against the frequencies, and, if necessary, non-correlating factors can be marked when a frequency is uncorrelated. -
FIG. 21 is a flow diagram illustrating an embodiment method for reporting power usage information, including locational data, data broken down by time intervals, frequency and power usage information per band, average power distribution, propagation models, atmospheric factors, which is capable of being represented graphical, quantitatively, qualitatively, and overlaid onto a geographic or topographic map. -
FIG. 22 is a flow diagram illustrating an embodiment method for creating frequency arrays. For each initialization, an embodiment of the invention will determine a center frequency, bandwidth, peak power, noise floor level, resolution bandwidth, power and date/time. Start and end frequencies are calculated using the bandwidth and center frequency and like frequencies are aggregated and sorted in order to produce a set of frequency arrays matching power measurements captured in each band. -
FIG. 23 is a flow diagram illustrating an embodiment method for reframe and aggregating power when producing frequency arrays. -
FIG. 24 is a flow diagram illustrating an embodiment method of reporting license expirations by accessing static or FCC databases. -
FIG. 25 is a flow diagram illustrating an embodiment method of reporting frequency power use in graphical, chart, or report format, with the option of adding frequencies from FCC or other databases. -
FIG. 26 is a flow diagram illustrating an embodiment method of connecting devices. After acquiring a GPS location, static and FCC databases are accessed to update license information, if available. A frequency scan will find open spaces and detect interferences and/or collisions. Based on the master device ID, set a random generated token to select channel form available channel model and continually transmit ID channel token. If node device reads ID, it will set itself to channel based on token and device will connect to master device. Master device will then set frequency and bandwidth channel. For each device connected to master, a frequency, bandwidth, and time slot in which to transmit is set. In one embodiment, these steps can be repeated until the max number of devices is connected. As new devices are connected, the device list is updated with channel model and the device is set as active. Disconnected devices are set as inactive. If collision occurs, update channel model and get new token channel. Active scans will search for new or lost devices and update devices list, channel model, and status accordingly. Channel model IDs are actively sent out for new or lost devices. -
FIG. 27 is a flow diagram illustrating an embodiment method of addressing collisions. -
FIG. 28 is a schematic diagram of an embodiment of the invention illustrating a virtualized computing network and a plurality of distributed devices.FIG. 28 is a schematic diagram of one embodiment of the present invention, illustrating components of a cloud-based computing system and network for distributed communication therewith by mobile communication devices.FIG. 28 illustrates an exemplary virtualized computing system for embodiments of the present invention loyalty and rewards platform. As illustrated inFIG. 28 , a basic schematic of some of the key components of a virtualized computing (or cloud-based) system according to the present invention are shown. Thesystem 2800 comprises at least oneremote server computer 2810 with aprocessing unit 2811 and memory. Theserver 2810 is constructed, configured and coupled to enable communication over anetwork 2850. The server provides for user interconnection with the server over the network with the at least one apparatus as described hereinabove 2840 positioned remotely from the server.Apparatus 2840 includes amemory 2846, aCPU 2844, anoperating system 2847, abus 2842, an input/output module 2848, and an output ordisplay 2849. Furthermore, the system is operable for a multiplicity of devices orapparatus embodiments displays network 2850 using the at least one device or apparatus for measuring signal emitting devices, each of the at least one apparatus is operable for network-based communication. Also, alternative architectures may be used instead of the client/server architecture. For example, a computer communications network, or other suitable architecture may be used. Thenetwork 2850 may be the Internet, an intranet, or any other network suitable for searching, obtaining, and/or using information and/or communications. The system of the present invention further includes anoperating system 2812 installed and running on the at least oneremote server 2810, enabling theserver 2810 to communicate throughnetwork 2850 with the remote, distributed devices or apparatus embodiments as described hereinabove, theserver 2810 having amemory 2820. The operating system may be any operating system known in the art that is suitable for network communication. -
FIG. 29 shows a schematic diagram illustrating aspects of the systems, methods and apparatus according to the present invention. Each node includes an apparatus or device unit, referenced in theFIG. 29 as “SigSet Device A”, “SigSet Device B”, “SigSet Device C”, and through “SigSet Device N” that are constructed and configured for selective exchange, both transmitting and receiving information over a network connection, either wired or wireless communications, with the master SigDB or database at a remote server location from the units. - The foregoing method descriptions and the process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the order of steps in the foregoing embodiments may be performed in any order. Words such as “thereafter,” “then,” “next,” etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods. Further, any reference to claim elements in the singular, for example, using the articles “a,” “an” or “the” is not to be construed as limiting the element to the singular.
- The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
- The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some steps or methods may be performed by circuitry that is specific to a given function.
- In one or more exemplary aspects, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable medium or non-transitory processor-readable medium. The steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module which may reside on a non-transitory computer-readable or processor-readable storage medium. Non-transitory computer-readable or processor-readable storage media may be any storage media that may be accessed by a computer or a processor. By way of example but not limitation, such non-transitory computer-readable or processor-readable media may include RAM, ROM, EEPROM, FLASH memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of non-transitory computer-readable and processor-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non-transitory processor-readable medium and/or computer-readable medium, which may be incorporated into a computer program product.
- Certain modifications and improvements will occur to those skilled in the art upon a reading of the foregoing description. The above-mentioned examples are provided to serve the purpose of clarifying the aspects of the invention and it will be apparent to one skilled in the art that they do not serve to limit the scope of the invention. All modifications and improvements have been deleted herein for the sake of conciseness and readability but are properly within the scope of the present invention.
Claims (20)
Priority Applications (17)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/983,678 US20160119806A1 (en) | 2013-03-15 | 2015-12-30 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US15/496,660 US10257727B2 (en) | 2013-03-15 | 2017-04-25 | Systems methods, and devices having databases and automated reports for electronic spectrum management |
US15/681,540 US10237770B2 (en) | 2013-03-15 | 2017-08-21 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US15/686,655 US10299149B2 (en) | 2013-03-15 | 2017-08-25 | Systems, methods, and devices for electronic spectrum management |
US16/353,811 US10492091B2 (en) | 2013-03-15 | 2019-03-14 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US16/371,615 US10531323B2 (en) | 2013-03-15 | 2019-04-01 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US16/383,054 US10694413B2 (en) | 2013-03-15 | 2019-04-12 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US16/415,549 US10623976B2 (en) | 2013-03-15 | 2019-05-17 | Systems, methods, and devices for electronic spectrum management |
US16/692,444 US10945146B2 (en) | 2013-03-15 | 2019-11-22 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US16/844,537 US11076308B2 (en) | 2013-03-15 | 2020-04-09 | Systems, methods, and devices for electronic spectrum management |
US16/906,716 US11259197B2 (en) | 2013-03-15 | 2020-06-19 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US17/191,215 US11234146B2 (en) | 2013-03-15 | 2021-03-03 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US17/381,961 US11653236B2 (en) | 2013-03-15 | 2021-07-21 | Systems, methods, and devices for electronic spectrum management |
US17/579,192 US11647409B2 (en) | 2013-03-15 | 2022-01-19 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US17/674,458 US20220174525A1 (en) | 2013-03-15 | 2022-02-17 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US18/086,196 US20230126223A1 (en) | 2013-03-15 | 2022-12-21 | Systems, methods, and devices for electronic spectrum management |
US18/142,892 US11930382B2 (en) | 2013-03-15 | 2023-05-03 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
Applications Claiming Priority (11)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201361789758P | 2013-03-15 | 2013-03-15 | |
US13/913,013 US9622041B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US13/912,683 US9288683B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US13/912,893 US9078162B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US14/082,873 US8805291B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/082,916 US8780968B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/082,930 US8824536B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/087,441 US8787836B1 (en) | 2013-03-15 | 2013-11-22 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US14/329,835 US8874044B1 (en) | 2013-03-15 | 2014-07-11 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US14/504,802 US9253673B2 (en) | 2013-03-15 | 2014-10-02 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US14/983,678 US20160119806A1 (en) | 2013-03-15 | 2015-12-30 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
Related Parent Applications (3)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/504,802 Continuation US9253673B2 (en) | 2013-03-15 | 2014-10-02 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US14/593,202 Continuation-In-Part US9749069B2 (en) | 2013-03-15 | 2015-01-09 | Systems, methods, and devices for electronic spectrum management |
US14/934,808 Continuation-In-Part US20160080955A1 (en) | 2013-03-15 | 2015-11-06 | Systems, methods, and devices for electronic spectrum management with remote access to data in a virtual computing network |
Related Child Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/412,982 Continuation-In-Part US10122479B2 (en) | 2013-03-15 | 2017-01-23 | Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum |
US15/496,660 Continuation-In-Part US10257727B2 (en) | 2013-03-15 | 2017-04-25 | Systems methods, and devices having databases and automated reports for electronic spectrum management |
Publications (1)
Publication Number | Publication Date |
---|---|
US20160119806A1 true US20160119806A1 (en) | 2016-04-28 |
Family
ID=51135713
Family Applications (26)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/913,013 Active 2033-10-06 US9622041B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US13/912,683 Active 2034-02-26 US9288683B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US13/912,893 Active 2034-01-24 US9078162B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US14/082,873 Active US8805291B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/082,930 Active US8824536B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/082,916 Active US8780968B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/273,193 Active US8868004B2 (en) | 2013-03-15 | 2014-05-08 | Systems, methods, and devices for electronic spectrum management |
US14/325,044 Active US8964824B2 (en) | 2013-03-15 | 2014-07-07 | Systems, methods, and devices for electronic spectrum management |
US14/329,815 Active US8885696B1 (en) | 2013-03-15 | 2014-07-11 | Systems, methods, and devices for electronic spectrum management |
US14/504,743 Active US9094974B2 (en) | 2013-03-15 | 2014-10-02 | Systems, methods, and devices for electronic spectrum management |
US14/504,770 Active US9094975B2 (en) | 2013-03-15 | 2014-10-02 | Systems, methods, and devices for electronic spectrum management |
US14/593,202 Active 2033-08-29 US9749069B2 (en) | 2013-03-15 | 2015-01-09 | Systems, methods, and devices for electronic spectrum management |
US14/743,011 Active US9414237B2 (en) | 2013-03-15 | 2015-06-18 | Systems, methods, and devices for electronic spectrum management |
US14/788,838 Active US9253648B2 (en) | 2013-03-15 | 2015-07-01 | Systems, methods, and devices for electronic spectrum management |
US14/788,842 Active US9420473B2 (en) | 2013-03-15 | 2015-07-01 | Systems, methods, and devices for electronic spectrum management |
US14/983,690 Abandoned US20160119794A1 (en) | 2013-03-15 | 2015-12-30 | Systems, methods, and devices for electronic spectrum management |
US14/983,678 Abandoned US20160119806A1 (en) | 2013-03-15 | 2015-12-30 | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US15/228,325 Active US9998243B2 (en) | 2013-03-15 | 2016-08-04 | Systems, methods, and devices for electronic spectrum management |
US15/236,524 Abandoned US20160374088A1 (en) | 2013-03-15 | 2016-08-15 | Systems, methods, and devices for electronic spectrum management |
US16/002,751 Active US10284309B2 (en) | 2013-03-15 | 2018-06-07 | Systems, methods, and devices for electronic spectrum management |
US16/395,987 Active US10554317B2 (en) | 2013-03-15 | 2019-04-26 | Systems, methods, and devices for electronic spectrum management |
US16/751,887 Active 2033-09-27 US11223431B2 (en) | 2013-03-15 | 2020-01-24 | Systems, methods, and devices for electronic spectrum management |
US17/569,984 Active US11588562B2 (en) | 2013-03-15 | 2022-01-06 | Systems, methods, and devices for electronic spectrum management |
US18/082,210 Active US11637641B1 (en) | 2013-03-15 | 2022-12-15 | Systems, methods, and devices for electronic spectrum management |
US18/137,785 Active US11791913B2 (en) | 2013-03-15 | 2023-04-21 | Systems, methods, and devices for electronic spectrum management |
US18/374,385 Active US11901963B1 (en) | 2013-03-15 | 2023-09-28 | Systems and methods for analyzing signals of interest |
Family Applications Before (16)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/913,013 Active 2033-10-06 US9622041B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US13/912,683 Active 2034-02-26 US9288683B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US13/912,893 Active 2034-01-24 US9078162B2 (en) | 2013-03-15 | 2013-06-07 | Systems, methods, and devices for electronic spectrum management |
US14/082,873 Active US8805291B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/082,930 Active US8824536B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/082,916 Active US8780968B1 (en) | 2013-03-15 | 2013-11-18 | Systems, methods, and devices for electronic spectrum management |
US14/273,193 Active US8868004B2 (en) | 2013-03-15 | 2014-05-08 | Systems, methods, and devices for electronic spectrum management |
US14/325,044 Active US8964824B2 (en) | 2013-03-15 | 2014-07-07 | Systems, methods, and devices for electronic spectrum management |
US14/329,815 Active US8885696B1 (en) | 2013-03-15 | 2014-07-11 | Systems, methods, and devices for electronic spectrum management |
US14/504,743 Active US9094974B2 (en) | 2013-03-15 | 2014-10-02 | Systems, methods, and devices for electronic spectrum management |
US14/504,770 Active US9094975B2 (en) | 2013-03-15 | 2014-10-02 | Systems, methods, and devices for electronic spectrum management |
US14/593,202 Active 2033-08-29 US9749069B2 (en) | 2013-03-15 | 2015-01-09 | Systems, methods, and devices for electronic spectrum management |
US14/743,011 Active US9414237B2 (en) | 2013-03-15 | 2015-06-18 | Systems, methods, and devices for electronic spectrum management |
US14/788,838 Active US9253648B2 (en) | 2013-03-15 | 2015-07-01 | Systems, methods, and devices for electronic spectrum management |
US14/788,842 Active US9420473B2 (en) | 2013-03-15 | 2015-07-01 | Systems, methods, and devices for electronic spectrum management |
US14/983,690 Abandoned US20160119794A1 (en) | 2013-03-15 | 2015-12-30 | Systems, methods, and devices for electronic spectrum management |
Family Applications After (9)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/228,325 Active US9998243B2 (en) | 2013-03-15 | 2016-08-04 | Systems, methods, and devices for electronic spectrum management |
US15/236,524 Abandoned US20160374088A1 (en) | 2013-03-15 | 2016-08-15 | Systems, methods, and devices for electronic spectrum management |
US16/002,751 Active US10284309B2 (en) | 2013-03-15 | 2018-06-07 | Systems, methods, and devices for electronic spectrum management |
US16/395,987 Active US10554317B2 (en) | 2013-03-15 | 2019-04-26 | Systems, methods, and devices for electronic spectrum management |
US16/751,887 Active 2033-09-27 US11223431B2 (en) | 2013-03-15 | 2020-01-24 | Systems, methods, and devices for electronic spectrum management |
US17/569,984 Active US11588562B2 (en) | 2013-03-15 | 2022-01-06 | Systems, methods, and devices for electronic spectrum management |
US18/082,210 Active US11637641B1 (en) | 2013-03-15 | 2022-12-15 | Systems, methods, and devices for electronic spectrum management |
US18/137,785 Active US11791913B2 (en) | 2013-03-15 | 2023-04-21 | Systems, methods, and devices for electronic spectrum management |
US18/374,385 Active US11901963B1 (en) | 2013-03-15 | 2023-09-28 | Systems and methods for analyzing signals of interest |
Country Status (1)
Country | Link |
---|---|
US (26) | US9622041B2 (en) |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9661604B1 (en) * | 2016-06-30 | 2017-05-23 | HawkEye 360, Inc. | Determining emitter locations |
US20170303014A1 (en) * | 2016-04-18 | 2017-10-19 | Karim Ghessassi | System for providing functionality based on sensor data |
US10313164B2 (en) * | 2016-09-27 | 2019-06-04 | Bae Systems Information And Electronic Systems Integration Inc. | Techniques for implementing a portable spectrum analyzer |
US20190215248A1 (en) * | 2017-06-28 | 2019-07-11 | Ciena Corporation | Multi-layer optical network management graphical user interface and visualizations |
US10466336B2 (en) | 2017-06-30 | 2019-11-05 | HawkEye 360, Inc. | Detecting radio signal emitter locations |
US10623215B2 (en) | 2013-11-18 | 2020-04-14 | Bae Systems Information And Electronic Systems Integration Inc. | Process for tunnelized cyclostationary to achieve low-energy spectrum sensing |
US20200389240A1 (en) * | 2019-06-04 | 2020-12-10 | Thayermahan, Inc. | Portable sensor fusion broadcast system for maritime situational awareness |
US10912003B1 (en) * | 2019-09-27 | 2021-02-02 | Fortinet, Inc. | Spectral efficient selection of station clusters for concurrent data transmissions in high efficiency WLANs (wireless local access networks) using unsupervised machine learning models |
US11101884B1 (en) | 2020-03-24 | 2021-08-24 | Ciena Corporation | Localizing anomalies of a fiber optic network within the context of a geographic map |
US11237277B2 (en) | 2019-02-15 | 2022-02-01 | Horizon Technologies Consultants, Ltd. | Techniques for determining geolocations |
US20220077941A1 (en) * | 2019-05-15 | 2022-03-10 | Astrapi Corporation | Frequency spectrum analyzers and devices, systems, software and methods for signal power measurement and spectrum analysis |
US11523370B1 (en) * | 2020-05-22 | 2022-12-06 | Bae Systems Information And Electronic Systems Integration Inc. | Efficient graphics processing unit (GPU) pulse detector |
US11711709B2 (en) | 2018-08-23 | 2023-07-25 | Tracfone Wireless, Inc. | System and process for using cellular connectivity analysis to determine optimal wireless equipment and service for a geographical area |
Families Citing this family (73)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9026157B1 (en) * | 2013-03-07 | 2015-05-05 | Sprint Communications Company L.P. | Identifying frequency band interference using a frequency activity record of a mobile device |
US10244504B2 (en) | 2013-03-15 | 2019-03-26 | DGS Global Systems, Inc. | Systems, methods, and devices for geolocation with deployable large scale arrays |
US10271233B2 (en) | 2013-03-15 | 2019-04-23 | DGS Global Systems, Inc. | Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum |
US10257728B2 (en) | 2013-03-15 | 2019-04-09 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management |
US10299149B2 (en) | 2013-03-15 | 2019-05-21 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management |
US9622041B2 (en) * | 2013-03-15 | 2017-04-11 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management |
US10257727B2 (en) | 2013-03-15 | 2019-04-09 | DGS Global Systems, Inc. | Systems methods, and devices having databases and automated reports for electronic spectrum management |
US10122479B2 (en) | 2017-01-23 | 2018-11-06 | DGS Global Systems, Inc. | Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum |
US11646918B2 (en) | 2013-03-15 | 2023-05-09 | Digital Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management for identifying open space |
US8750156B1 (en) | 2013-03-15 | 2014-06-10 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management for identifying open space |
US10219163B2 (en) | 2013-03-15 | 2019-02-26 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management |
US10231206B2 (en) | 2013-03-15 | 2019-03-12 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management for identifying signal-emitting devices |
US10237770B2 (en) | 2013-03-15 | 2019-03-19 | DGS Global Systems, Inc. | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US10257729B2 (en) | 2013-03-15 | 2019-04-09 | DGS Global Systems, Inc. | Systems, methods, and devices having databases for electronic spectrum management |
IN2014CH00806A (en) * | 2014-02-19 | 2015-08-28 | Proxim Wireless Corp | |
US10506053B2 (en) * | 2014-03-07 | 2019-12-10 | Comcast Cable Communications, Llc | Location aware security system |
LT3114884T (en) | 2014-03-07 | 2020-02-10 | Ubiquiti Inc. | Cloud device identification and authentication |
PL2943001T3 (en) * | 2014-05-08 | 2018-01-31 | Icomera Ab | Method and system for network error detection |
JP6449568B2 (en) * | 2014-06-24 | 2019-01-09 | 京セラ株式会社 | Mobile terminal and control method |
WO2016003862A1 (en) | 2014-06-30 | 2016-01-07 | Ubiquiti Networks, Inc. | Methods and tools for assisting in the configuration of a wireless radio network using functional maps |
ES2873999T3 (en) | 2014-08-31 | 2021-11-04 | Ubiquiti Inc | Methods and devices for monitoring and improving the status of a wireless network |
CN112074004B (en) | 2014-11-06 | 2023-07-14 | 日本电气株式会社 | Radio terminal, radio station and method thereof |
EP3094021B1 (en) * | 2015-05-12 | 2017-09-27 | Televés, S.A. | System for processing of telecommunication signals |
US10122440B2 (en) * | 2015-06-24 | 2018-11-06 | Hughes Network Systems, Llc | Remote spectrum analysis |
WO2017014011A1 (en) * | 2015-07-17 | 2017-01-26 | 株式会社村田製作所 | Position detection system and computer program |
CN105188082B (en) * | 2015-08-05 | 2018-06-29 | 重庆邮电大学 | For the evaluation method of RSS/AOA/TDOA positioning performances under indoor WLAN environment |
US9619977B2 (en) | 2015-08-27 | 2017-04-11 | Trident Holding, LLC | Deployable beacon |
PL3353989T3 (en) | 2015-09-25 | 2021-08-30 | Ubiquiti Inc. | Compact and integrated key controller apparatus for monitoring networks |
US10368246B2 (en) | 2015-10-26 | 2019-07-30 | The Research Foundation For The State University Of New York | Methods and systems for spectrum management |
US9986438B2 (en) | 2016-01-22 | 2018-05-29 | Microsoft Technology Licensing, Llc | Hierarchical spectrum coordination |
US10582401B2 (en) | 2016-03-08 | 2020-03-03 | Aurora Insight Inc. | Large scale radio frequency signal information processing and analysis system |
US10779179B2 (en) | 2016-03-08 | 2020-09-15 | Aurora Insight Inc. | System and method for large-scale radio frequency signal collection and processing |
CN107371165A (en) * | 2016-05-13 | 2017-11-21 | 索尼公司 | Spectrum management apparatus and method, electronic installation and method and wireless communication system |
JP6696667B2 (en) * | 2016-07-04 | 2020-05-20 | アンリツ株式会社 | Signal analysis device and signal analysis method |
CN106228240B (en) * | 2016-07-30 | 2020-09-01 | 复旦大学 | Deep convolution neural network implementation method based on FPGA |
US10429836B2 (en) * | 2016-11-14 | 2019-10-01 | Electronics And Telecommunications Research Institute | Channel access method in unmanned aerial vehicle (UAV) control and non-payload communication (CNPC) system |
CN109716358B (en) * | 2016-12-30 | 2023-06-06 | 同济大学 | Method for detecting pedestrian flow by using WI-FI probe |
ES2929894T3 (en) | 2017-01-23 | 2022-12-02 | Digital Global Systems Inc | Systems, methods and devices for automatic signal detection with extraction of temporal characteristics within a spectrum |
US10498951B2 (en) | 2017-01-23 | 2019-12-03 | Digital Global Systems, Inc. | Systems, methods, and devices for unmanned vehicle detection |
US10529241B2 (en) | 2017-01-23 | 2020-01-07 | Digital Global Systems, Inc. | Unmanned vehicle recognition and threat management |
US10459020B2 (en) | 2017-01-23 | 2019-10-29 | DGS Global Systems, Inc. | Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time within a spectrum |
US10700794B2 (en) | 2017-01-23 | 2020-06-30 | Digital Global Systems, Inc. | Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time within an electromagnetic spectrum |
US11950117B2 (en) * | 2017-05-02 | 2024-04-02 | Aurora Insight Inc. | Large scale radio frequency signal information processing and analysis system |
CN108933706B (en) * | 2017-05-23 | 2022-02-25 | 华为技术有限公司 | Method, device and system for monitoring data traffic |
WO2019029825A1 (en) * | 2017-08-11 | 2019-02-14 | Nokia Technologies Oy | Indicating ue capability with short tti |
CN107729078B (en) * | 2017-09-30 | 2019-12-03 | Oppo广东移动通信有限公司 | Background application management-control method, device, storage medium and electronic equipment |
CN107734513B (en) * | 2017-10-18 | 2021-03-02 | 中国联合网络通信集团有限公司 | Method and device for determining service density |
US11483691B2 (en) * | 2018-03-13 | 2022-10-25 | Cypress Semiconductor Corporation | Time of arrival estimation for Bluetooth systems and devices |
CN108680916B (en) * | 2018-05-18 | 2022-01-25 | 云南电网有限责任公司电力科学研究院 | Distance measurement method and system for power transmission line and communication antenna on power iron tower |
US10616801B1 (en) * | 2018-06-04 | 2020-04-07 | Sprint Spectrum L.P. | Systems and methods for dynamic inter band carrier aggregation |
US10880678B1 (en) * | 2018-08-07 | 2020-12-29 | Nestwave Sas | Indoor and outdoor geolocation and time of arrival estimation |
US10943461B2 (en) | 2018-08-24 | 2021-03-09 | Digital Global Systems, Inc. | Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time |
CN109614952B (en) * | 2018-12-27 | 2020-08-25 | 成都数之联科技有限公司 | Target signal detection and identification method based on waterfall plot |
US11121785B2 (en) | 2019-01-10 | 2021-09-14 | Exfo Inc. | Detection and tracking of interferers in a RF spectrum with multi-lane processing |
US10735109B1 (en) | 2019-01-11 | 2020-08-04 | Exfo Inc. | Automated analysis of RF spectrum |
US11323352B2 (en) * | 2019-01-30 | 2022-05-03 | Rohde & Schwarz Gmbh & Co. Kg | Test system and test method |
US11129032B2 (en) | 2019-11-26 | 2021-09-21 | Motorola Mobility Llc | Optimal device position for wireless communication |
US10849034B1 (en) * | 2019-11-26 | 2020-11-24 | Motorola Mobility Llc | Signal map for wireless connectivity |
US11432109B2 (en) * | 2019-11-27 | 2022-08-30 | Qualcomm Incorporated | Positioning of vehicles and pedestrians leveraging ranging signal |
US11540234B2 (en) | 2019-12-05 | 2022-12-27 | Exfo Inc. | Automated narrow peak interference severity estimation |
US11363466B2 (en) * | 2020-01-22 | 2022-06-14 | Charter Communications Operating, Llc | Methods and apparatus for antenna optimization in a quasi-licensed wireless system |
WO2021216034A1 (en) * | 2020-04-20 | 2021-10-28 | Bae Systems Information And Electronic Systems Integration Inc. | Id ambiguity reduction |
US11665547B2 (en) | 2020-05-01 | 2023-05-30 | Digital Global Systems, Inc. | System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization |
US11653213B2 (en) | 2020-05-01 | 2023-05-16 | Digital Global Systems. Inc. | System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization |
US11638160B2 (en) | 2020-05-01 | 2023-04-25 | Digital Global Systems, Inc. | System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization |
US11395149B2 (en) | 2020-05-01 | 2022-07-19 | Digital Global Systems, Inc. | System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization |
US11700533B2 (en) | 2020-05-01 | 2023-07-11 | Digital Global Systems, Inc. | System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization |
US11323176B2 (en) * | 2020-10-01 | 2022-05-03 | Viavi Solutions Inc | Modular cell site installation, testing, measurement, and maintenance tool |
CN113112071A (en) * | 2021-04-14 | 2021-07-13 | 张敏 | Point type economic management service system |
CN113438042B (en) * | 2021-05-10 | 2023-03-28 | 中国科学院新疆天文台 | Real-time electromagnetic environment monitoring system and method |
CN113377872B (en) * | 2021-06-25 | 2024-02-27 | 北京红山信息科技研究院有限公司 | Offline synchronization method, device and equipment of online system data in big data center |
CN114710831B (en) * | 2022-03-10 | 2023-12-08 | 南京市地铁交通设施保护办公室 | RFID label positioning system based on deep learning |
CN114499702B (en) * | 2022-03-28 | 2022-07-12 | 成都锢德科技有限公司 | Portable real-time signal acquisition, analysis and recognition system |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140064723A1 (en) * | 2012-05-01 | 2014-03-06 | The Johns Hopkins University | Cueing System for Universal Optical Receiver |
Family Cites Families (482)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US3001901A (en) | 1955-12-01 | 1961-09-26 | Libbey Owens Ford Glass Co | Method of producing electrically conductive articles |
US3006195A (en) | 1958-07-28 | 1961-10-31 | Statham Instrument Inc | Gage |
US3023957A (en) | 1958-09-22 | 1962-03-06 | Robert M Goodman | Apparatus for accumulating numerical data |
US4215345A (en) | 1978-08-31 | 1980-07-29 | Nasa | Interferometric locating system |
US4400700A (en) | 1981-06-08 | 1983-08-23 | The United States Of America As Represented By The Secretary Of The Army | Doppler frequency analysis of radar signals |
US4453137A (en) | 1982-02-05 | 1984-06-05 | The United States Of America As Represented By The Secretary Of The Army | Signal processor for plural frequency detection and tracking over predetermined range of frequencies |
CA1187944A (en) | 1982-09-15 | 1985-05-28 | Her Majesty The Queen, In Right Of Canada, As Represented By The Ministe R Of National Defence | Spectrum surveillance receiver system |
US4638493A (en) | 1985-06-17 | 1987-01-20 | Sperry Corporation | Adaptive interference rejection for improved frequency hop detection |
US5393713A (en) | 1987-07-27 | 1995-02-28 | Prs Corporation | Broadcast receiver capable of automatic station identification and format-scanning based on an internal database updatable over the airwaves with automatic receiver location determination |
US4928106A (en) | 1988-07-14 | 1990-05-22 | Ashtech Telesis, Inc. | Global positioning system receiver with improved radio frequency and digital processing |
US5446756A (en) | 1990-03-19 | 1995-08-29 | Celsat America, Inc. | Integrated cellular communications system |
US5835857A (en) | 1990-03-19 | 1998-11-10 | Celsat America, Inc. | Position determination for reducing unauthorized use of a communication system |
US5230087A (en) | 1990-09-12 | 1993-07-20 | Belar Electronics Laboratory, Inc. | Device for measuring various characteristics of a radio frequency signal |
US5506864A (en) | 1990-12-05 | 1996-04-09 | Interdigital Technology Corporation | CDMA communications and geolocation system and method |
US5134407A (en) | 1991-04-10 | 1992-07-28 | Ashtech Telesis, Inc. | Global positioning system receiver digital processing technique |
US5548809A (en) | 1992-07-15 | 1996-08-20 | Southwestern Bell Technology Resources, Inc. | Spectrum sharing communications system and system for monitoring available spectrum |
US5343212A (en) * | 1992-12-11 | 1994-08-30 | Litton Industries, Inc. | (AOA/LBI) emitter ranging method and apparatus |
JPH07106989A (en) | 1993-09-30 | 1995-04-21 | Sony Corp | Receiving device |
US5570099A (en) * | 1993-10-15 | 1996-10-29 | Loral Federal Systems Company | TDOA/FDOA technique for locating a transmitter |
US6983051B1 (en) * | 1993-11-18 | 2006-01-03 | Digimarc Corporation | Methods for audio watermarking and decoding |
US6418131B1 (en) | 1994-06-17 | 2002-07-09 | Lake Communications Limited | Spectrum monitoring for PSTN subscribers |
US5589835A (en) | 1994-12-20 | 1996-12-31 | Trimble Navigation Limited | Differential GPS receiver system linked by infrared signals |
US5973601A (en) * | 1995-12-06 | 1999-10-26 | Campana, Jr.; Thomas J. | Method of radio transmission between a radio transmitter and radio receiver |
US5642732A (en) * | 1995-05-03 | 1997-07-01 | Acuson Corporation | Apparatus and method for estimating missing doppler signals and spectra |
US6049535A (en) | 1996-06-27 | 2000-04-11 | Interdigital Technology Corporation | Code division multiple access (CDMA) communication system |
AU708274B2 (en) * | 1995-09-20 | 1999-07-29 | Kratos Integral Holdings, Llc | Locating the source of an unknown signal |
JPH09178833A (en) | 1995-12-28 | 1997-07-11 | Sony Corp | Terminal device |
US5831874A (en) | 1996-05-31 | 1998-11-03 | Motorola, Inc. | Method and system for calculating a transmitted signal characteristic in an environmental model |
US5856803A (en) | 1996-07-24 | 1999-01-05 | Pevler; A. Edwin | Method and apparatus for detecting radio-frequency weapon use |
EP0828225A1 (en) | 1996-09-04 | 1998-03-11 | Siemens Aktiengesellschaft | Process and means for analysing EEG data |
WO1998010307A1 (en) | 1996-09-09 | 1998-03-12 | Dennis Jay Dupray | Location of a mobile station |
US6249252B1 (en) | 1996-09-09 | 2001-06-19 | Tracbeam Llc | Wireless location using multiple location estimators |
US6107910A (en) | 1996-11-29 | 2000-08-22 | X-Cyte, Inc. | Dual mode transmitter/receiver and decoder for RF transponder tags |
US6898197B1 (en) | 1997-02-28 | 2005-05-24 | Interdigital Technology Corporation | Geolocation of a mobile terminal in a CDMA communication system |
BR9804923A (en) | 1997-05-19 | 2001-09-18 | Integrated Data Communications | System and process for communicating geo-positioning data on three geometrical axes, by time, within telecommunication networks |
US6134445A (en) | 1997-07-24 | 2000-10-17 | Lucent Technologies, Inc. | Wireless terminal adapted for measuring signal propagation characteristics |
FI114422B (en) | 1997-09-04 | 2004-10-15 | Nokia Corp | Source speech activity detection |
US6085090A (en) | 1997-10-20 | 2000-07-04 | Motorola, Inc. | Autonomous interrogatable information and position device |
US6286021B1 (en) | 1997-10-22 | 2001-09-04 | Texas Instruments Incorporated | Apparatus and method for a reduced complexity tap leakage unit in a fast adaptive filter circuit |
KR100248671B1 (en) | 1997-12-29 | 2000-04-01 | 이계철 | Method of predicting transmission loss for micro/pico-cell in the system of designing wireless network |
US5936575A (en) | 1998-02-13 | 1999-08-10 | Science And Applied Technology, Inc. | Apparatus and method for determining angles-of-arrival and polarization of incoming RF signals |
US6039692A (en) * | 1998-06-19 | 2000-03-21 | Vingmed Sound A/S | Method and apparatus for processing ultrasound signals |
US6115580A (en) | 1998-09-08 | 2000-09-05 | Motorola, Inc. | Communications network having adaptive network link optimization using wireless terrain awareness and method for use therein |
US6512788B1 (en) | 1998-11-02 | 2003-01-28 | Agilent Technologies, Inc. | RF output spectrum measurement analyzer and method |
CA2260336A1 (en) | 1999-02-15 | 2000-08-15 | Robert Inkol | Modulation recognition system |
US6985437B1 (en) * | 1999-05-25 | 2006-01-10 | 3Com Corporation | Method for dynamic performance optimization in a data-over-cable system |
US6304760B1 (en) | 1999-06-11 | 2001-10-16 | Lucent Technologies, Inc. | Method for reducing the effect of atmospheric ducting on wireless transmissions |
US6490318B1 (en) | 1999-06-24 | 2002-12-03 | Agere Systems Inc. | Phase-compensating constant modulus algorithm |
US6296612B1 (en) | 1999-07-09 | 2001-10-02 | General Electric Company | Method and apparatus for adaptive wall filtering in spectral Doppler ultrasound imaging |
GB9919525D0 (en) * | 1999-08-19 | 1999-10-20 | Secr Defence | Method and apparatus for locating the source of an unknown signal |
US6191731B1 (en) | 1999-08-25 | 2001-02-20 | Trimble Navigation Limited | GPS receiver having a fast time to first fix |
US6160511A (en) | 1999-09-30 | 2000-12-12 | Motorola, Inc. | Method and apparatus for locating a remote unit within a communication system |
US6859831B1 (en) | 1999-10-06 | 2005-02-22 | Sensoria Corporation | Method and apparatus for internetworked wireless integrated network sensor (WINS) nodes |
US6677895B1 (en) | 1999-11-16 | 2004-01-13 | Harris Corporation | System and method for determining the location of a transmitting mobile unit |
US7003414B1 (en) | 1999-11-30 | 2006-02-21 | Agilent Technologies, Inc. | Monitoring system and method implementing failure time spectrum scan |
US6898235B1 (en) | 1999-12-10 | 2005-05-24 | Argon St Incorporated | Wideband communication intercept and direction finding device using hyperchannelization |
JP4038009B2 (en) | 2000-02-03 | 2008-01-23 | 株式会社アドバンテスト | Correlation function measurement method and apparatus |
US6339396B1 (en) | 2000-02-17 | 2002-01-15 | Lockheed Martin Corp | Location of the radio frequency emitting targets |
US6628231B2 (en) | 2000-02-17 | 2003-09-30 | Lockheed Martin Corp. | Location of radio frequency emitting targets |
US6850557B1 (en) | 2000-04-18 | 2005-02-01 | Sirf Technology, Inc. | Signal detector and method employing a coherent accumulation system to correlate non-uniform and disjoint sample segments |
WO2001086492A1 (en) | 2000-05-05 | 2001-11-15 | Abm Industries Pty. Ltd. | End user to mobile service provider message exchange system based on proximity |
US7366463B1 (en) | 2000-05-05 | 2008-04-29 | The Directv Group, Inc. | Military UHF and commercial Geo-mobile system combination for radio signal relay |
US6904269B1 (en) | 2000-06-02 | 2005-06-07 | Tektronix, Inc. | Signal type identification |
US10641861B2 (en) | 2000-06-02 | 2020-05-05 | Dennis J. Dupray | Services and applications for a communications network |
US7146176B2 (en) | 2000-06-13 | 2006-12-05 | Shared Spectrum Company | System and method for reuse of communications spectrum for fixed and mobile applications with efficient method to mitigate interference |
US6711404B1 (en) | 2000-07-21 | 2004-03-23 | Scoreboard, Inc. | Apparatus and method for geostatistical analysis of wireless signal propagation |
US6925070B2 (en) | 2000-07-31 | 2005-08-02 | Ipr Licensing, Inc. | Time-slotted data packets with a preamble |
US6707419B2 (en) | 2000-08-16 | 2004-03-16 | Raytheon Company | Radar transmitter circuitry and techniques |
US6785321B1 (en) | 2000-10-31 | 2004-08-31 | Motorola, Inc. | Apparatus and method for estimating the time of arrival of a spread spectrum signal in a wireless communication system |
US7054863B2 (en) | 2000-11-15 | 2006-05-30 | Pacific Datavision, Inc. | System and method for originating, storing, processing and delivering message data |
US7016722B2 (en) | 2000-11-20 | 2006-03-21 | New York University | System and method for fetal brain monitoring |
US6492945B2 (en) | 2001-01-19 | 2002-12-10 | Massachusetts Institute Of Technology | Instantaneous radiopositioning using signals of opportunity |
JP3607208B2 (en) | 2001-02-26 | 2005-01-05 | 株式会社東芝 | Wireless terminal certification test system |
US6834194B2 (en) | 2001-03-09 | 2004-12-21 | Denso Corporation | Relative future activity indicators for assisting in selecting the source of received communications |
US6876326B2 (en) | 2001-04-23 | 2005-04-05 | Itt Manufacturing Enterprises, Inc. | Method and apparatus for high-accuracy position location using search mode ranging techniques |
CA2390253A1 (en) | 2001-06-11 | 2002-12-11 | Unique Broadband Systems, Inc. | Ofdm multiple sub-channel communication system |
US6861982B2 (en) | 2001-08-16 | 2005-03-01 | Itt Manufacturing Enterprises, Inc. | System for determining position of an emitter |
US6917328B2 (en) | 2001-11-13 | 2005-07-12 | Rosum Corporation | Radio frequency device for receiving TV signals and GPS satellite signals and performing positioning |
US6879840B2 (en) | 2001-11-30 | 2005-04-12 | M2 Networks, Inc. | Method and apparatus for adaptive QoS-based joint rate and power control algorithm in multi-rate wireless systems |
US6771957B2 (en) | 2001-11-30 | 2004-08-03 | Interdigital Technology Corporation | Cognition models for wireless communication systems and method and apparatus for optimal utilization of a radio channel based on cognition model data |
GB0203621D0 (en) | 2002-02-15 | 2002-04-03 | Bae Systems Defence Sysytems L | Emitter location system |
US7152025B2 (en) | 2002-02-28 | 2006-12-19 | Texas Instruments Incorporated | Noise identification in a communication system |
US6850735B2 (en) | 2002-04-22 | 2005-02-01 | Cognio, Inc. | System and method for signal classiciation of signals in a frequency band |
US20050003828A1 (en) | 2002-04-09 | 2005-01-06 | Sugar Gary L. | System and method for locating wireless devices in an unsynchronized wireless environment |
US6714605B2 (en) | 2002-04-22 | 2004-03-30 | Cognio, Inc. | System and method for real-time spectrum analysis in a communication device |
US7151938B2 (en) | 2002-04-15 | 2006-12-19 | America Online, Inc. | Dynamically managing and reconfiguring wireless mesh networks |
US7424268B2 (en) | 2002-04-22 | 2008-09-09 | Cisco Technology, Inc. | System and method for management of a shared frequency band |
US7292656B2 (en) | 2002-04-22 | 2007-11-06 | Cognio, Inc. | Signal pulse detection scheme for use in real-time spectrum analysis |
US7116943B2 (en) | 2002-04-22 | 2006-10-03 | Cognio, Inc. | System and method for classifying signals occuring in a frequency band |
US7254191B2 (en) | 2002-04-22 | 2007-08-07 | Cognio, Inc. | System and method for real-time spectrum analysis in a radio device |
US7269151B2 (en) | 2002-04-22 | 2007-09-11 | Cognio, Inc. | System and method for spectrum management of a shared frequency band |
US6741595B2 (en) | 2002-06-11 | 2004-05-25 | Netrake Corporation | Device for enabling trap and trace of internet protocol communications |
US7099367B2 (en) | 2002-06-14 | 2006-08-29 | Time Domain Corporation | Method and apparatus for converting RF signals to baseband |
US20040208238A1 (en) | 2002-06-25 | 2004-10-21 | Thomas John K. | Systems and methods for location estimation in spread spectrum communication systems |
US7171161B2 (en) | 2002-07-30 | 2007-01-30 | Cognio, Inc. | System and method for classifying signals using timing templates, power templates and other techniques |
US7408907B2 (en) | 2002-09-11 | 2008-08-05 | Cisco Technology, Inc. | System and method for management of a shared frequency band using client-specific management techniques |
US7555262B2 (en) | 2002-09-24 | 2009-06-30 | Honeywell International Inc. | Radio frequency interference monitor |
US7016673B2 (en) | 2002-10-01 | 2006-03-21 | Interdigital Technology Corporation | Wireless communication method and system with controlled WTRU peer-to-peer communications |
CN2579099Y (en) | 2002-11-05 | 2003-10-08 | 浙江大学 | USB communication interface device of real-time signal analyzer |
US7480512B2 (en) | 2004-01-16 | 2009-01-20 | Bones In Motion, Inc. | Wireless device, program products and methods of using a wireless device to deliver services |
DE60335496D1 (en) | 2003-01-30 | 2011-02-03 | Fujitsu Ltd | FADING FREQUENCY ESTIMATION DEVICE |
US6991514B1 (en) | 2003-02-21 | 2006-01-31 | Verity Instruments, Inc. | Optical closed-loop control system for a CMP apparatus and method of manufacture thereof |
US7187326B2 (en) | 2003-03-28 | 2007-03-06 | Harris Corporation | System and method for cumulant-based geolocation of cooperative and non-cooperative RF transmitters |
WO2004095758A2 (en) | 2003-04-22 | 2004-11-04 | Cognio, Inc. | Signal classification methods for scanning receiver and other applications |
US8138972B2 (en) | 2003-09-02 | 2012-03-20 | Csr Technology Inc. | Signal processing system for satellite positioning signals |
US7049965B2 (en) | 2003-10-02 | 2006-05-23 | General Electric Company | Surveillance systems and methods |
US7110756B2 (en) | 2003-10-03 | 2006-09-19 | Cognio, Inc. | Automated real-time site survey in a shared frequency band environment |
US6968185B2 (en) | 2003-11-05 | 2005-11-22 | Interdigital Technology Corporation | Mobile wireless presence and situation management system and method |
US20050104773A1 (en) | 2003-11-17 | 2005-05-19 | Clarke Christopher J.M. | Mobile radiation surveillance network |
KR20050048414A (en) | 2003-11-19 | 2005-05-24 | 삼성전자주식회사 | Relay method for connection request between wireless devices in the wireless network and apparatus thereof |
US7519371B2 (en) | 2004-02-09 | 2009-04-14 | Qualcomm Incorporated | Multi-hop communications in a wireless network |
US7260408B2 (en) * | 2004-02-20 | 2007-08-21 | Airespace, Inc. | Wireless node location mechanism using antenna pattern diversity to enhance accuracy of location estimates |
US7801490B1 (en) | 2004-03-17 | 2010-09-21 | Hewlett-Packard Company | Interference based scheduling using cognitive radios |
US7460837B2 (en) | 2004-03-25 | 2008-12-02 | Cisco Technology, Inc. | User interface and time-shifted presentation of data in a system that monitors activity in a shared radio frequency band |
CA2501003C (en) * | 2004-04-23 | 2009-05-19 | F. Hoffmann-La Roche Ag | Sample analysis to provide characterization data |
US7162207B2 (en) | 2004-06-21 | 2007-01-09 | Elektrobit Oy | System, apparatus, method and computer program for producing signals for testing radio frequency communication devices |
US7236128B2 (en) | 2004-06-23 | 2007-06-26 | Cognio, Inc. | System and method for locating radio emitters using self-calibrated path loss computation |
US7817014B2 (en) | 2004-07-30 | 2010-10-19 | Reva Systems Corporation | Scheduling in an RFID system having a coordinated RFID tag reader array |
EP1810182A4 (en) | 2004-08-31 | 2010-07-07 | Kumar Gopalakrishnan | Method and system for providing information services relevant to visual imagery |
ES2397074T3 (en) | 2004-10-13 | 2013-03-04 | Mcmaster University | Transmission power control techniques for wireless communication systems |
US7873515B2 (en) * | 2004-11-23 | 2011-01-18 | Stmicroelectronics Asia Pacific Pte. Ltd. | System and method for error reconstruction of streaming audio information |
JP4864901B2 (en) | 2004-11-30 | 2012-02-01 | アドバンスド・バイオニクス・アクチエンゲゼルシャフト | Implantable actuator for hearing aid |
US20060128311A1 (en) | 2004-12-13 | 2006-06-15 | Yohannes Tesfai | Matching receive signal strenth data associated with radio emission sources for positioning applications |
US7466960B2 (en) | 2005-02-08 | 2008-12-16 | Cisco Technology, Inc. | Cognitive spectrum analysis and information display techniques |
US7620396B2 (en) | 2005-02-08 | 2009-11-17 | Cisco Technology, Inc. | Monitoring for radio frequency activity violations in a licensed frequency band |
US7271576B1 (en) | 2005-03-01 | 2007-09-18 | O'harra Ii Dale G | Hand held antenna/network impedance analyzer |
US7433652B2 (en) | 2005-03-07 | 2008-10-07 | Polaris Wireless, Inc. | Electro-magnetic propagation modeling |
US7728755B1 (en) | 2005-03-16 | 2010-06-01 | Damjan Jocic | Reactive parallel processing jamming system |
US9420423B1 (en) | 2005-04-12 | 2016-08-16 | Ehud Mendelson | RF beacon deployment and method of use |
US7176833B2 (en) | 2005-04-26 | 2007-02-13 | Sony Ericsson Mobile Communications Ab | Portable electronic devices, methods and computer program products using activity-triggered GPS updates |
US7865140B2 (en) | 2005-06-14 | 2011-01-04 | The Invention Science Fund I, Llc | Device pairing via intermediary device |
US8249028B2 (en) | 2005-07-22 | 2012-08-21 | Sri International | Method and apparatus for identifying wireless transmitters |
US7280810B2 (en) | 2005-08-03 | 2007-10-09 | Kamilo Feher | Multimode communication system |
CN100544677C (en) * | 2005-08-16 | 2009-09-30 | 深圳迈瑞生物医疗电子股份有限公司 | Handle the method for Doppler signal gap |
US20070076657A1 (en) | 2005-09-01 | 2007-04-05 | Cypress Semiconductor Corporation | Method for channel agility in wireless access points |
US7522917B1 (en) | 2005-09-09 | 2009-04-21 | Agilent Technologies, Inc. | System and method for visualization of connectivity relationships in a wireless system |
US7733224B2 (en) | 2006-06-30 | 2010-06-08 | Bao Tran | Mesh network personal emergency response appliance |
US7684467B2 (en) | 2005-10-28 | 2010-03-23 | Silicon Laboratories Inc. | Performing blind scanning in a receiver |
US8358723B1 (en) | 2005-11-12 | 2013-01-22 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Self-configurable radio receiver system and method for use with signals without prior knowledge of signal defining characteristics |
US7689240B2 (en) | 2005-11-16 | 2010-03-30 | Trueposition, Inc. | Transmit-power control for wireless mobile services |
US8006195B1 (en) | 2005-11-28 | 2011-08-23 | Meta Greek, LLC | Spectrum analyzer interface |
US7877698B1 (en) | 2006-10-06 | 2011-01-25 | Meta Geek, LLC | Spectrum analyzer user interface |
US7459898B1 (en) | 2005-11-28 | 2008-12-02 | Ryan Woodings | System and apparatus for detecting and analyzing a frequency spectrum |
US8576231B2 (en) | 2005-11-28 | 2013-11-05 | Ryan Woodings | Spectrum analyzer interface |
US7702044B2 (en) | 2005-12-05 | 2010-04-20 | Marvell World Trade, Ltd. | Radar detection and dynamic frequency selection |
US7904097B2 (en) | 2005-12-07 | 2011-03-08 | Ekahau Oy | Location determination techniques |
US7969311B2 (en) | 2005-12-15 | 2011-06-28 | Invisitrack, Inc. | Multi-path mitigation in rangefinding and tracking objects using reduced attenuation RF technology |
US7676192B1 (en) | 2005-12-21 | 2010-03-09 | Radio Shack, Corp. | Radio scanner programmed from frequency database and method |
US20080133190A1 (en) | 2006-02-13 | 2008-06-05 | Shay Peretz | method and a system for planning a security array of sensor units |
US7593814B2 (en) | 2006-02-24 | 2009-09-22 | Tektronix, Inc. | Attaching measurement data to an area map |
US20070223419A1 (en) | 2006-03-24 | 2007-09-27 | Samsung Electronics Co., Ltd. | Method and system for sharing spectrum in a wireless communications network |
US20070233409A1 (en) | 2006-04-04 | 2007-10-04 | Boyan Corydon J | Spectrum analyzer with cascadable trace math functions |
US7933344B2 (en) | 2006-04-25 | 2011-04-26 | Mircosoft Corporation | OFDMA based on cognitive radio |
US9658341B2 (en) * | 2006-04-28 | 2017-05-23 | Telecommunication Systems, Inc. | GNSS long-code acquisition, ambiguity resolution, and signal validation |
US7835319B2 (en) | 2006-05-09 | 2010-11-16 | Cisco Technology, Inc. | System and method for identifying wireless devices using pulse fingerprinting and sequence analysis |
US8055204B2 (en) | 2007-08-15 | 2011-11-08 | Shared Spectrum Company | Methods for detecting and classifying signals transmitted over a radio frequency spectrum |
US8326313B2 (en) | 2006-05-12 | 2012-12-04 | Shared Spectrum Company | Method and system for dynamic spectrum access using detection periods |
US8184653B2 (en) | 2007-08-15 | 2012-05-22 | Shared Spectrum Company | Systems and methods for a cognitive radio having adaptable characteristics |
US7551139B1 (en) * | 2006-05-12 | 2009-06-23 | Northrop Grumman Corporation | Multi-platform precision passive location of continuous wave emitters |
US8155649B2 (en) | 2006-05-12 | 2012-04-10 | Shared Spectrum Company | Method and system for classifying communication signals in a dynamic spectrum access system |
US9538388B2 (en) | 2006-05-12 | 2017-01-03 | Shared Spectrum Company | Method and system for dynamic spectrum access |
US8027249B2 (en) | 2006-10-18 | 2011-09-27 | Shared Spectrum Company | Methods for using a detector to monitor and detect channel occupancy |
US7564816B2 (en) | 2006-05-12 | 2009-07-21 | Shared Spectrum Company | Method and system for determining spectrum availability within a network |
FR2902259B1 (en) | 2006-06-09 | 2008-07-18 | Thales Sa | EXTRACTION AND ANALYSIS SYSTEM OF RADIO ELECTRIC SIGNALS OF INTERESTS |
US7920469B2 (en) | 2006-06-15 | 2011-04-05 | Alcatel-Lucent Usa Inc. | Indicating a variable control channel structure for transmissions in a cellular system |
US8688759B2 (en) | 2006-06-16 | 2014-04-01 | Bae Systems Information And Electronic Systems Integration Inc. | Efficient detection algorithm system for a broad class of signals using higher-order statistics in time as well as frequency domains |
US8619909B2 (en) | 2006-06-20 | 2013-12-31 | Southwest Research Institute | Signal detector using matched filter for training signal detection |
US7603245B2 (en) | 2006-06-20 | 2009-10-13 | Southwest Research Institute | Blind estimation of bandwidth and duration parameters of an incoming signal |
US8077089B2 (en) * | 2006-08-15 | 2011-12-13 | Rincon Research Corporation | Precision geolocation of moving or fixed transmitters using multiple observers |
US8131239B1 (en) | 2006-08-21 | 2012-03-06 | Vadum, Inc. | Method and apparatus for remote detection of radio-frequency devices |
US8064391B2 (en) | 2006-08-22 | 2011-11-22 | Embarq Holdings Company, Llc | System and method for monitoring and optimizing network performance to a wireless device |
US8520606B2 (en) | 2006-10-23 | 2013-08-27 | Samsung Electronics Co., Ltd | Synchronous spectrum sharing based on OFDM/OFDMA signaling |
GB2443226B (en) * | 2006-10-28 | 2011-08-17 | Qinetiq Ltd | Method and apparatus for locating the source of an unknown signal |
US8116697B2 (en) | 2006-11-10 | 2012-02-14 | Xirrus, Inc. | System and method for reducing multi-modulation radio transmit range |
US8289907B2 (en) | 2006-11-10 | 2012-10-16 | Powerwave Cognition, Inc. | Interference avoidance for autonomous dynamic spectrum access systems |
IL179678A0 (en) | 2006-11-28 | 2008-01-20 | Israel Aerospace Ind Ltd | Airport anti-collision system and method |
US7558685B2 (en) | 2006-11-29 | 2009-07-07 | Samplify Systems, Inc. | Frequency resolution using compression |
US8879573B2 (en) | 2006-12-01 | 2014-11-04 | Microsoft Corporation | Media access control (MAC) protocol for cognitive wireless networks |
EP3247146B1 (en) | 2007-01-04 | 2020-04-29 | Qualcomm Incorporated | Method and apparatus for distributed spectrum sensing for wireless communication |
US8295859B1 (en) | 2007-01-23 | 2012-10-23 | University Of South Florida | System and method of exploiting location awareness to improve wireless cognitive radio |
US7471245B2 (en) * | 2007-01-31 | 2008-12-30 | L3 Communications Integrated Systems, L.P. | Method and apparatus for estimating geolocations |
US7453400B2 (en) * | 2007-02-02 | 2008-11-18 | Bae Systems Information And Electronic Systems Integration Inc. | Multiplatform TDOA correlation interferometer geolocation |
US7555412B2 (en) | 2007-02-09 | 2009-06-30 | Microsoft Corporation | Communication efficient spatial search in a sensor data web portal |
US20080207136A1 (en) | 2007-02-20 | 2008-08-28 | Haiyun Tang | High Dynamic Range Tranceiver for Cognitive Radio |
JP2008204582A (en) | 2007-02-22 | 2008-09-04 | Elpida Memory Inc | Nonvolatile ram |
US20080211481A1 (en) | 2007-03-02 | 2008-09-04 | Mark Star Servo-Tech Co., Ltd. | Handheld spectral scanner |
US8515473B2 (en) | 2007-03-08 | 2013-08-20 | Bae Systems Information And Electronic Systems Integration Inc. | Cognitive radio methodology, physical layer policies and machine learning |
WO2008121878A1 (en) | 2007-03-28 | 2008-10-09 | Proximetry, Inc. | Systems and methods for distance measurement in wireless networks |
US7667640B2 (en) * | 2007-04-13 | 2010-02-23 | Glowlink Communications Technology, Inc. | Determining a geolocation solution of an emitter on earth using satellite signals |
US7876869B1 (en) | 2007-05-23 | 2011-01-25 | Hypers, Inc. | Wideband digital spectrometer |
US8280433B2 (en) | 2007-05-29 | 2012-10-02 | Dell Products L.P. | Database for antenna system matching for wireless communications in portable information handling systems |
US20200162890A1 (en) | 2007-06-06 | 2020-05-21 | Datavalet Technologies | System and method for wireless device detection, recognition and visit profiling |
WO2008154647A1 (en) | 2007-06-12 | 2008-12-18 | Triage Wireless, Inc. | Vital sign monitor for cufflessly measuring blood pressure corrected for vascular index |
US7885819B2 (en) * | 2007-06-29 | 2011-02-08 | Microsoft Corporation | Bitstream syntax for multi-process audio decoding |
US7920990B2 (en) | 2007-07-16 | 2011-04-05 | Tektronix, Inc. | Apparatus and methods of defining spectral regions of interest for signal analysis |
WO2009012354A1 (en) * | 2007-07-17 | 2009-01-22 | Clemson University | System and method to assess signal similarity with applications to diagnostics and prognostics |
US8559571B2 (en) | 2007-08-17 | 2013-10-15 | Ralink Technology Corporation | Method and apparatus for beamforming of multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) transceivers |
IL203785A (en) | 2007-09-12 | 2014-07-31 | Qualcomm Inc | Capacity increasing devices and methods for wireless communication |
US7626546B2 (en) * | 2007-09-27 | 2009-12-01 | L-3 Communications Integrated Systems L.P. | Methods and systems for detection and location of multiple emitters |
JP4872871B2 (en) | 2007-09-27 | 2012-02-08 | ソニー株式会社 | Sound source direction detecting device, sound source direction detecting method, and sound source direction detecting camera |
US8160839B1 (en) | 2007-10-16 | 2012-04-17 | Metageek, Llc | System and method for device recognition based on signal patterns |
FR2923106B1 (en) | 2007-10-24 | 2010-01-01 | Commissariat Energie Atomique | METHOD FOR SEARCHING FREE TAPE FOR OPPORTUNISTIC TELECOMMUNICATION TERMINAL. |
US8014345B2 (en) | 2007-10-31 | 2011-09-06 | Motorola Solutions, Inc. | Incumbent spectrum hold device |
US8437700B2 (en) | 2007-11-09 | 2013-05-07 | Bae Systems Information And Electronic Systems Integration Inc. | Protocol reference model, security and inter-operability in a cognitive communications system |
US8116792B2 (en) * | 2007-11-20 | 2012-02-14 | At&T Intellectual Property I, Lp | Methods, systems, and computer-readable media for mitigating a temporary interference condition |
US8151311B2 (en) | 2007-11-30 | 2012-04-03 | At&T Intellectual Property I, L.P. | System and method of detecting potential video traffic interference |
TWI444010B (en) | 2007-12-06 | 2014-07-01 | Koninkl Philips Electronics Nv | Channel management method in a distributed spectrum cognitive radio network |
US20090149202A1 (en) | 2007-12-07 | 2009-06-11 | Christian Steele | System and method for determination of position |
US8718838B2 (en) | 2007-12-14 | 2014-05-06 | The Boeing Company | System and methods for autonomous tracking and surveillance |
GB0725053D0 (en) | 2007-12-21 | 2008-01-30 | Fujitsu Lab Of Europ Ltd | Communications system |
US7595754B2 (en) | 2007-12-24 | 2009-09-29 | Qualcomm Incorporated | Methods, systems and apparatus for integrated wireless device location determination |
JP4388118B2 (en) | 2007-12-26 | 2009-12-24 | 株式会社東芝 | Modulation method estimation apparatus and method |
KR20090078944A (en) | 2008-01-16 | 2009-07-21 | 경남대학교 산학협력단 | Remote upgrade system for spectrum analyzer using the smart packet |
US8081567B2 (en) | 2008-01-30 | 2011-12-20 | Alcatel Lucent | Method and apparatus for detecting wireless data subscribers using natted devices |
US7965641B2 (en) | 2008-02-14 | 2011-06-21 | Lingna Holdings Pte., Llc | Robust cooperative spectrum sensing for cognitive radios |
US8094610B2 (en) | 2008-02-25 | 2012-01-10 | Virginia Tech Intellectual Properties, Inc. | Dynamic cellular cognitive system |
US8059694B2 (en) | 2008-03-11 | 2011-11-15 | Nokia Corporation | Method, apparatus and computer program to efficiently acquire signals in a cognitive radio environment |
US8155039B2 (en) | 2008-03-17 | 2012-04-10 | Wi-Lan, Inc. | System and apparatus for cascading and redistributing HDTV signals |
EP2255583A2 (en) | 2008-03-18 | 2010-12-01 | Koninklijke Philips Electronics N.V. | Distributed spectrum sensing |
US7800541B2 (en) | 2008-03-31 | 2010-09-21 | Golba Llc | Methods and systems for determining the location of an electronic device |
MX2010010913A (en) | 2008-04-04 | 2010-12-21 | Powerwave Cognition Inc | Methods and systems for a mobile, broadband, routable internet. |
US8811331B2 (en) * | 2008-04-10 | 2014-08-19 | Telefonaktiebolaget L M Ericsson (Publ) | Pilot design using costas arrays |
US20090282130A1 (en) | 2008-05-12 | 2009-11-12 | Nokia Corporation | Resource sharing via close-proximity wireless communication |
US8285321B2 (en) | 2008-05-15 | 2012-10-09 | Qualcomm Incorporated | Method and apparatus for using virtual noise figure in a wireless communication network |
US8315237B2 (en) | 2008-10-29 | 2012-11-20 | Google Inc. | Managing and monitoring emergency services sector resources |
WO2009140669A2 (en) | 2008-05-16 | 2009-11-19 | Terahop Networks, Inc. | Securing, monitoring and tracking shipping containers |
US8060104B2 (en) | 2008-05-30 | 2011-11-15 | Motorola Solutions, Inc. | Coexistence and incumbent protection in a cognitive radio network |
US20100020707A1 (en) | 2008-06-04 | 2010-01-28 | Ryan Winfield Woodings | Wi-fi sensor |
EP2314017B1 (en) | 2008-06-18 | 2018-09-05 | SpiderCloud Wireless, Inc. | Methods and apparatus for coordinating network monitoring and/or automating device confirgurations based on monitoring results |
US8369305B2 (en) | 2008-06-30 | 2013-02-05 | Cisco Technology, Inc. | Correlating multiple detections of wireless devices without a unique identifier |
US7692573B1 (en) | 2008-07-01 | 2010-04-06 | The United States Of America As Represented By The Secretary Of The Navy | System and method for classification of multiple source sensor measurements, reports, or target tracks and association with uniquely identified candidate targets |
US8027690B2 (en) | 2008-08-05 | 2011-09-27 | Qualcomm Incorporated | Methods and apparatus for sensing the presence of a transmission signal in a wireless channel |
US8229368B1 (en) | 2008-08-18 | 2012-07-24 | Eden Rock Communications, Llc | Systems for detecting and locating interference sources |
EP2319260A2 (en) | 2008-08-19 | 2011-05-11 | Shared Spectrum Company | Method and system for dynamic spectrum access using specialty detectors and improved networking |
JP2010071977A (en) | 2008-08-20 | 2010-04-02 | Seiko Epson Corp | Initial position determination method, positioning method, and positioning device |
US8170577B2 (en) | 2008-08-22 | 2012-05-01 | Telcom Ventures, Llc | Method and system enabling use of white space radio spectrum using digital broadcast signals |
US8103213B2 (en) | 2008-09-03 | 2012-01-24 | Nokia Corporation | Software-defined radio configuration |
US8193981B1 (en) | 2008-09-26 | 2012-06-05 | Rockwell Collins, Inc. | Coordinated sensing and precision geolocation of target emitter |
KR20110071105A (en) | 2008-09-30 | 2011-06-28 | 스파이더클라우드 와이어리스, 인크. | Dynamic topological adaptation |
US8001902B2 (en) | 2008-10-09 | 2011-08-23 | The United States Of America As Represented By The Secretary Of The Navy | Signal transmission surveillance system |
US7893875B1 (en) * | 2008-10-31 | 2011-02-22 | The United States Of America As Represented By The Director National Security Agency | Device for and method of geolocation |
US8107391B2 (en) | 2008-11-19 | 2012-01-31 | Wi-Lan, Inc. | Systems and etiquette for home gateways using white space |
CN102257735B (en) | 2008-12-17 | 2015-07-22 | 翔跃通信公司 | Base station with coordinated multiple air-interface operations |
US8138975B2 (en) | 2008-12-30 | 2012-03-20 | Trueposition, Inc. | Interference detection, characterization and location in a wireless communications or broadcast system |
US20100172443A1 (en) | 2009-01-07 | 2010-07-08 | Qualcomm Incorporated | Systems and methods of classifying and decoding wireless signals |
US8335204B2 (en) | 2009-01-30 | 2012-12-18 | Wi-Lan, Inc. | Wireless local area network using TV white space spectrum and long term evolution system architecture |
US8290503B2 (en) | 2009-02-01 | 2012-10-16 | Qualcomm Incorporated | Multichannel dynamic frequency selection |
US7911385B2 (en) * | 2009-02-27 | 2011-03-22 | Harris Corporation | RF transmitter geolocation system and related methods |
US8120488B2 (en) | 2009-02-27 | 2012-02-21 | Rf Controls, Llc | Radio frequency environment object monitoring system and methods of use |
US8326309B2 (en) | 2009-03-06 | 2012-12-04 | Bae Systems Information And Electronic Systems Integration Inc. | Resource allocation in co-existence mode |
US8112238B1 (en) | 2009-03-24 | 2012-02-07 | Agilent Technologies, Inc. | Method of alignment for radio frequency measurement instrument and measurement instrument including same |
US8238247B2 (en) | 2009-03-25 | 2012-08-07 | Wi-Lan, Inc. | System and method for proactive repeat transmission of data over an unreliable transmission medium |
US8416710B2 (en) | 2009-03-30 | 2013-04-09 | At&T Mobility Ii Llc | Indoor competitive survey of wireless networks |
US8155603B2 (en) | 2009-04-02 | 2012-04-10 | Clearwire Ip Holdings Llc | System and method of unlicensed bi-directional communications over an ultra-high frequency (UHF) band reserved for licensed communications |
US8514729B2 (en) | 2009-04-03 | 2013-08-20 | Airmagnet, Inc. | Method and system for analyzing RF signals in order to detect and classify actively transmitting RF devices |
AR076204A1 (en) | 2009-04-06 | 2011-05-26 | Interdigital Patent Holdings | TELEVISION BAND (TVBD) SILENCING CHANNEL THROUGH DIFFERENT RADIO ACCESS TECHNOLOGIES |
US8213874B2 (en) | 2009-04-06 | 2012-07-03 | Progeny Lms, Llc | System and method for dynamic frequency assignment |
KR101594124B1 (en) | 2009-04-09 | 2016-02-16 | 삼성전자주식회사 | Non-volatile ram solid state dirve including the same and computer system including the same |
US20110053604A1 (en) | 2009-04-16 | 2011-03-03 | Byoung Hoon Kim | Scheduling method based on hierarchical cell structure and femto base station for the same |
US8213868B2 (en) | 2009-04-17 | 2012-07-03 | Lingna Holdings Pte., Llc | Exploiting multiple antennas for spectrum sensing in cognitive radio networks |
US8483155B1 (en) | 2009-05-06 | 2013-07-09 | Marvell International Ltd. | Using television whitespace spectrum for wireless local area networks |
WO2010128908A1 (en) | 2009-05-08 | 2010-11-11 | Telefonaktiebolaget L M Ericsson (Publ) | Allocation of primary and secondary synchronization code sequences to cells in a wireless communication system |
US20100306249A1 (en) | 2009-05-27 | 2010-12-02 | James Hill | Social network systems and methods |
CN101909302B (en) | 2009-06-03 | 2013-10-09 | 华为技术有限公司 | Method and equipment for distributing dynamic spectrums |
US20100309317A1 (en) | 2009-06-04 | 2010-12-09 | Wi-Lan Inc. | Device and method for detecting unused tv spectrum for wireless communication systems |
US8134493B2 (en) | 2009-07-02 | 2012-03-13 | Raytheon Company | System and method for precision geolocation utilizing multiple sensing modalities |
US8463195B2 (en) | 2009-07-22 | 2013-06-11 | Qualcomm Incorporated | Methods and apparatus for spectrum sensing of signal features in a wireless channel |
JP5609252B2 (en) | 2009-07-31 | 2014-10-22 | ソニー株式会社 | Transmission power allocation method, communication apparatus, and program |
JP5531767B2 (en) | 2009-07-31 | 2014-06-25 | ソニー株式会社 | Transmission power control method, communication apparatus, and program |
US8565811B2 (en) | 2009-08-04 | 2013-10-22 | Microsoft Corporation | Software-defined radio using multi-core processor |
JP5353812B2 (en) | 2009-08-12 | 2013-11-27 | ソニー株式会社 | COMMUNICATION CONTROL METHOD, COMMUNICATION DEVICE, AND PROGRAM |
US8373759B2 (en) | 2009-08-18 | 2013-02-12 | Wi-Lan, Inc. | White space spectrum sensor for television band devices |
US8892050B2 (en) | 2009-08-18 | 2014-11-18 | Qualcomm Incorporated | Sensing wireless communications in television frequency bands |
US8688132B2 (en) | 2009-09-07 | 2014-04-01 | Telefonaktiebolaget L M Ericsson (Publ) | Sensing wireless transmissions from a licensed user of a licensed spectral resource |
EP2299757A3 (en) | 2009-09-22 | 2014-08-27 | Nokia Corporation | Cognitive control radio access information via database or cognitive pilot channel |
US8174444B2 (en) * | 2009-09-26 | 2012-05-08 | Rincon Research Corporation | Method of correlating known image data of moving transmitters with measured radio signals |
US8233928B2 (en) | 2009-09-29 | 2012-07-31 | Spectrum Bridge, Inc. | System and method for managing spectrum allocation |
US8976302B2 (en) | 2009-09-30 | 2015-03-10 | Wi-Lan, Inc. | Radio frequency front end for television band receiver and spectrum sensor |
US8350970B2 (en) | 2009-09-30 | 2013-01-08 | Wi-Lan, Inc. | Radio frequency front end for television band receiver and spectrum sensor |
US8589359B2 (en) | 2009-10-12 | 2013-11-19 | Motorola Solutions, Inc. | Method and apparatus for automatically ensuring consistency among multiple spectrum databases |
US8873524B2 (en) | 2009-10-27 | 2014-10-28 | At&T Intellectual Property I, L.P. | Method and apparatus for providing channel sharing among white space networks |
CN102598851B (en) | 2009-11-10 | 2015-02-11 | 高知有限公司 | Device and method for heating using RF energy |
US8625553B2 (en) | 2009-11-17 | 2014-01-07 | At&T Intellectual Property I, L.P. | Method and apparatus for providing communication over a white space channel without causing interference |
US20110117869A1 (en) | 2009-11-18 | 2011-05-19 | Ryan Woodings | Multiple band portable spectrum analyzer |
US8644230B2 (en) | 2009-11-23 | 2014-02-04 | At&T Intellectual Property I, L.P. | Method and apparatus for providing communication over a white space channel without causing interference to digital television systems |
US8315571B2 (en) | 2009-11-24 | 2012-11-20 | Telefonaktiebolaget L M Ericsson (Publ) | Sensing wireless transmissions from a user of a spectral resource |
KR101271430B1 (en) | 2009-11-30 | 2013-06-05 | 한국전자통신연구원 | Method and apparatus for detecting received signal in wireless communication systems |
US8477711B2 (en) * | 2009-12-04 | 2013-07-02 | General Electric Company | Media access control scheme for a multi-frequency TDMA network |
US8352223B1 (en) * | 2009-12-18 | 2013-01-08 | The Boeing Company | Network communications testbed |
KR101321773B1 (en) | 2009-12-21 | 2013-10-25 | 한국전자통신연구원 | Apparatus and method for signal detecting in a common frequency band |
US8843155B2 (en) | 2010-01-20 | 2014-09-23 | Airpatrol Corporation | Multi-band radio frequency detection and location system |
US8600312B2 (en) | 2010-01-25 | 2013-12-03 | Qualcomm Incorporated | Method and apparatus for spectral sensing |
KR101142344B1 (en) | 2010-01-25 | 2012-06-13 | 티더블유모바일 주식회사 | Emergency signal transmission system using of a mobile phone and method of the same |
US8886794B2 (en) | 2010-01-28 | 2014-11-11 | Thinkrf Corporation | System and method for detecting RF transmissions in frequency bands of interest across a geographic area |
US8279823B2 (en) | 2010-02-09 | 2012-10-02 | Spectrum Bridge, Inc. | Spectrum allocation system and method |
US8400292B2 (en) | 2010-03-01 | 2013-03-19 | Andrew Llc | System and method for location of mobile devices in confined environments |
US8311483B2 (en) | 2010-03-09 | 2012-11-13 | Telefonaktiebolaget L M Ericsson (Publ) | Radio white space sensing |
CN105744484A (en) | 2010-03-10 | 2016-07-06 | 交互数字专利控股公司 | Location determination of infrastructure device and terminal device |
WO2011156029A2 (en) | 2010-03-12 | 2011-12-15 | Bae Systems Information And Electronic Systems Integration Inc. | Method and system to make current wireless radios cognitive using an external sensor and application level messaging |
US8442541B2 (en) | 2010-03-29 | 2013-05-14 | Ntt Docomo, Inc. | System and method for inter-cell interference avoidance in co-channel networks |
US8988272B2 (en) | 2010-03-30 | 2015-03-24 | Escort Inc. | Digital receiver techniques in radar detectors |
US8526974B2 (en) | 2010-04-12 | 2013-09-03 | Telefonaktiebolaget L M Ericsson (Publ) | Locating a source of wireless transmissions from a licensed user of a licensed spectral resource |
US8718673B2 (en) | 2010-05-21 | 2014-05-06 | Maple Acquisition Llc | System and method for location assurance of a mobile device |
KR101141888B1 (en) | 2010-05-24 | 2012-05-03 | 포항공과대학교 산학협력단 | Method and device of signal presence detection in the radio communication system based on cognitive radio |
US8364188B2 (en) | 2010-06-04 | 2013-01-29 | Alcatel Lucent | Method and controller for allocating whitespace spectrum |
JP5624675B2 (en) | 2010-06-07 | 2014-11-12 | エルジー エレクトロニクスインコーポレイティド | Method and apparatus for operating a station in a WLAN system |
US20120140236A1 (en) | 2010-06-14 | 2012-06-07 | S2 Corporation | Spatial Spectral Photonic Receiver for Direction Finding via Wideband Phase Sensitive Spectral Mapping |
CN102474729B (en) | 2010-07-09 | 2016-04-27 | Wi-Lan有限公司 | Use the TV vacancy Space Facilities of structured database |
US8717929B2 (en) | 2010-07-15 | 2014-05-06 | Rivada Networks Llc | Methods and systems for dynamic spectrum arbitrage |
US8934373B2 (en) | 2010-07-15 | 2015-01-13 | Rivada Networks, Llc | Methods and systems for mutiRAN dynamic spectrum arbitrage |
US8711721B2 (en) | 2010-07-15 | 2014-04-29 | Rivada Networks Llc | Methods and systems for dynamic spectrum arbitrage |
US8861452B2 (en) | 2010-08-16 | 2014-10-14 | Qualcomm Incorporated | Method and apparatus for use of licensed spectrum for control channels in cognitive radio communications |
WO2012030190A2 (en) | 2010-09-03 | 2012-03-08 | 한국전자통신연구원 | System and method for managing resources in a communication system |
US8494464B1 (en) | 2010-09-08 | 2013-07-23 | Rockwell Collins, Inc. | Cognitive networked electronic warfare |
WO2012039653A1 (en) | 2010-09-20 | 2012-03-29 | Telefonaktiebolaget L M Ericsson (Publ) | Reducing interference in a radio access network |
US8320910B2 (en) | 2010-09-22 | 2012-11-27 | Xg Technology, Inc. | Band masking of self organizing cellular networks |
EP2620028B1 (en) | 2010-09-23 | 2020-04-29 | BlackBerry Limited | System and method for dynamic coordination of radio resources usage in a wireless network environment |
US8792900B2 (en) | 2010-09-23 | 2014-07-29 | Nokia Corporation | Autonomous unlicensed band reuse in mixed cellular and device-to-device network |
US8326240B1 (en) | 2010-09-27 | 2012-12-04 | Rockwell Collins, Inc. | System for specific emitter identification |
US9588218B2 (en) * | 2010-09-30 | 2017-03-07 | Echo Ridge Llc | System and method for robust navigation and geolocation using measurements of opportunity |
US10212687B2 (en) * | 2010-09-30 | 2019-02-19 | Echo Ridge Llc | System and method for robust navigation and geolocation using measurements of opportunity |
US8174931B2 (en) | 2010-10-08 | 2012-05-08 | HJ Laboratories, LLC | Apparatus and method for providing indoor location, position, or tracking of a mobile computer using building information |
TWI524799B (en) | 2010-10-12 | 2016-03-01 | 內數位專利控股公司 | Service-based approach to channel selection and network configuration for television white space networks |
CN102457856B (en) | 2010-10-15 | 2015-12-16 | 电信科学技术研究院 | A kind ofly realize based on CR method, the Apparatus and system that frequency moves |
US8421440B2 (en) | 2010-10-25 | 2013-04-16 | Nokia Corporation | Apparatus for spectrum sensing and associated methods |
US8710847B2 (en) | 2010-10-28 | 2014-04-29 | Donald Marvin | Self-correcting amplifier system |
JP5660445B2 (en) | 2010-11-05 | 2015-01-28 | 独立行政法人情報通信研究機構 | Wireless device and communication method |
US8958834B2 (en) | 2010-11-08 | 2015-02-17 | Spidercloud Wireless, Inc. | Resource control in a communication system |
KR101881414B1 (en) | 2010-11-10 | 2018-08-24 | 한국전자통신연구원 | System and method for managing resource in communication system |
CN105722051A (en) | 2010-11-16 | 2016-06-29 | 交互数字专利控股公司 | Method For Wireless Direct Link Operation And Central Entity |
US8954065B2 (en) | 2010-11-24 | 2015-02-10 | Lg Electronics Inc. | Method of communicating data based on an unlicensed band in a wireless communication system |
CA2818888C (en) | 2010-11-26 | 2017-08-15 | Research In Motion Limited | Radiation pattern recognition system and method for a mobile communications device |
US20120302263A1 (en) | 2010-11-29 | 2012-11-29 | Qualcomm Incorporated | Estimating access terminal location based on uplink signals |
US9504035B2 (en) | 2010-12-02 | 2016-11-22 | Nec Corporation | Communication terminal, channel selection method, and program |
US9332439B2 (en) | 2010-12-08 | 2016-05-03 | Microsoft Technology Licensing, Llc | Coexistence of white space devices and wireless narrowband devices |
TWI412023B (en) | 2010-12-14 | 2013-10-11 | Univ Nat Chiao Tung | A microphone array structure and method for noise reduction and enhancing speech |
US9134442B2 (en) | 2010-12-16 | 2015-09-15 | Bp Corporation North America Inc. | Seismic acquisition using narrowband seismic sources |
US8504087B2 (en) | 2010-12-17 | 2013-08-06 | Spectrum Bridge, Inc. | System and method for controlling access to spectrum for wireless communications |
US8532686B2 (en) | 2010-12-24 | 2013-09-10 | Spectrum Bridge, Inc. | System and method for managing spectrum resources |
US8755275B2 (en) | 2010-12-29 | 2014-06-17 | Electronics And Telecommunications Research Institute | System and method for managing resource in communication system |
US20230087729A1 (en) | 2010-12-30 | 2023-03-23 | Staton Techiya Llc | Information processing using a population of data acquisition devices |
US8406780B2 (en) | 2011-01-14 | 2013-03-26 | Intel Mobile Communications GmbH | LTE operation in white spaces |
US11265652B2 (en) | 2011-01-25 | 2022-03-01 | Sonos, Inc. | Playback device pairing |
US8868133B1 (en) * | 2011-02-24 | 2014-10-21 | Corvas Technologies Corp | Beacon and associated components for a ranging system |
WO2012121476A1 (en) | 2011-03-10 | 2012-09-13 | 엘지전자 주식회사 | Method and apparatus for transreceiving data in medical body area network |
KR101717081B1 (en) | 2011-03-23 | 2017-03-28 | 삼성전자주식회사 | Storage device comprising a buffer memory by using a nonvolatile-ram and volatile-ram |
US9124347B2 (en) | 2011-04-04 | 2015-09-01 | Qualcomm Incorporated | Systems and methods for communication in a white space |
US8676144B2 (en) | 2011-04-14 | 2014-03-18 | Cisco Technology, Inc. | Adaptive interference nulling for MIMO receiver based on interference characteristics |
KR101394606B1 (en) | 2011-04-14 | 2014-05-13 | 주식회사 케이티 | Operation and maintenance system for supporting scalable bandwidth, and femtocell ap thereof |
US20120275354A1 (en) | 2011-04-26 | 2012-11-01 | Nxp B.V. | Asymmetric white space communications |
US20120282942A1 (en) | 2011-05-02 | 2012-11-08 | Nokia Siemens Networks Oy | Methods, apparatuses and computer program products for configuring frequency aggregation |
KR101521375B1 (en) | 2011-05-04 | 2015-05-18 | 엠파이어 테크놀로지 디벨롭먼트 엘엘씨 | A method for distributed interference coordination in a femtocell environment |
WO2012156574A1 (en) | 2011-05-13 | 2012-11-22 | Nokia Corporation | Interference management in wireless network |
US8731474B2 (en) | 2011-05-25 | 2014-05-20 | Shared Spectrum Company | Method and system for man-made noise rejection detector |
US9007262B1 (en) * | 2011-05-25 | 2015-04-14 | Leidos, Inc. | Diversified doppler for single platform geolocation |
US8615190B2 (en) * | 2011-05-31 | 2013-12-24 | Exelis Inc. | System and method for allocating jamming energy based on three-dimensional geolocation of emitters |
US8928759B2 (en) | 2011-06-08 | 2015-01-06 | Aurora Wireless, Inc. | System and method of implementing a cognitive radio device with enhanced spectrum sensing |
WO2012176217A1 (en) * | 2011-06-20 | 2012-12-27 | Muthukumar Prasad | Smart active antenna radiation pattern optimising system for mobile devices achieved by sensing device proximity environment with property, position, orientation, signal quality and operating modes |
EP3349535B1 (en) | 2011-06-24 | 2020-02-19 | Interdigital Patent Holdings, Inc. | Method and apparatus for supporting wideband and multiple bandwidth transmission protocols |
US20130005374A1 (en) | 2011-06-28 | 2013-01-03 | Nokia Corporation | Method and apparatus for providing spectrum reservation |
US8718560B2 (en) | 2011-07-07 | 2014-05-06 | Cisco Technology, Inc. | Dynamic clear channel assessment using spectrum intelligent interference nulling |
US8666319B2 (en) | 2011-07-15 | 2014-03-04 | Cisco Technology, Inc. | Mitigating effects of identified interference with adaptive CCA threshold |
US8818437B2 (en) | 2011-08-02 | 2014-08-26 | Cisco Technology, Inc. | Narrowband interference avoidance for dynamic channel assignment |
US8831625B2 (en) | 2011-08-03 | 2014-09-09 | Spectrum Bridge, Inc. | Systems and methods for processing spectrum coexistence information to optimize spectrum allocation |
JP6166260B2 (en) | 2011-08-03 | 2017-07-19 | ポルテ・コーポレイションPoLTE Corporation | Multipath mitigation in object ranging and tracking using RF techniques with reduced attenuation |
US20130053054A1 (en) | 2011-08-24 | 2013-02-28 | Microsoft Corporation | Using predictive technology to intelligently choose communication |
US8639178B2 (en) | 2011-08-30 | 2014-01-28 | Clear Channel Management Sevices, Inc. | Broadcast source identification based on matching broadcast signal fingerprints |
US8675781B2 (en) | 2011-09-08 | 2014-03-18 | Thinkrf Corporation | Radio frequency receiver system for wideband signal processing |
JP5610400B2 (en) | 2011-09-20 | 2014-10-22 | 株式会社Pfu | Node detection apparatus, node detection method, and program |
US8554264B1 (en) | 2011-11-17 | 2013-10-08 | Michael L. Gibbons | Systems and methods for optimizing broadcasts |
JP2015506604A (en) | 2011-12-22 | 2015-03-02 | インターデイジタル パテント ホールディングス インコーポレイテッド | Method, apparatus and system for dynamic spectrum allocation |
JP5921345B2 (en) | 2012-01-13 | 2016-05-24 | 株式会社日立国際電気 | Multi-channel wireless communication system, base station, channel utilization method |
US8700077B2 (en) | 2012-01-17 | 2014-04-15 | Spectrum Bridge, Inc. | System and method for determining noise floor in a wireless communications environment |
EP2807850A2 (en) | 2012-01-26 | 2014-12-03 | Interdigital Patent Holdings, Inc. | Dynamic parameter adjustment for lte coexistence |
US9363002B2 (en) | 2012-03-08 | 2016-06-07 | Telefonaktiebolaget Lm Ericsson (Publ) | Precoding with partially stale feedback |
US8989683B2 (en) | 2012-03-27 | 2015-03-24 | Bae Systems Information And Electronic Systems Integration Inc. | Ultra-wideband high power amplifier architecture |
US8866672B2 (en) * | 2012-04-05 | 2014-10-21 | L-3 Communications Integrated Systems Lp | Cooperative systems and methods for TDOA-based emitter location |
US8675789B2 (en) * | 2012-04-23 | 2014-03-18 | Cambridge Silicon Radio Limited | Receiver with variable gain elements and automatic gain control to maintain a positive signal to noise ratio margin |
FR2990273B1 (en) * | 2012-05-04 | 2014-05-09 | Commissariat Energie Atomique | METHOD AND DEVICE FOR DETECTING FREQUENCY BANDWAY IN FREQUENCY BAND AND COMMUNICATION EQUIPMENT COMPRISING SUCH A DEVICE |
WO2013177578A1 (en) | 2012-05-25 | 2013-11-28 | Eden Rock Communications, Llc | System and methods for cellular/satellite network coexistence |
US20130331114A1 (en) | 2012-06-06 | 2013-12-12 | Eden Rock Communications, Llc | Adjacent network aware self organizing network system |
US20170024767A1 (en) | 2012-07-12 | 2017-01-26 | William V Johnson, JR. | Technology System to Develop and Support Community News Services with Multi-Dimensional Marketing and Distributed Computing. |
US9011338B2 (en) * | 2012-07-12 | 2015-04-21 | Siemens Medical Solutions Usa, Inc. | Gap filling for spectral doppler ultrasound |
US11557179B2 (en) | 2012-07-19 | 2023-01-17 | Philip Paul Givant | Specialized slot machine for conducting a wagering fantasy sports tournament |
US9094834B2 (en) | 2012-09-11 | 2015-07-28 | Microsoft Technology Licensing, Llc | White space utilization |
US9223007B2 (en) * | 2012-11-21 | 2015-12-29 | Raytheon Company | Kalman filtering with indirect noise measurements |
US20140223336A1 (en) * | 2013-01-17 | 2014-08-07 | Social Order, LLC | System and method of providing communication recommendations |
US9245378B1 (en) * | 2013-01-18 | 2016-01-26 | Rockwell Collins, Inc. | Surface data generating system, device, and method |
US9819441B2 (en) | 2013-01-21 | 2017-11-14 | Spectrum Effect, Inc. | Method for uplink jammer detection and avoidance in long-term evolution (LTE) networks |
US10104559B2 (en) | 2013-01-21 | 2018-10-16 | Spectrum Effect, Inc. | Method for downlink jammer detection and avoidance in long-term evolution (LTE) networks |
US20140206307A1 (en) | 2013-01-22 | 2014-07-24 | Michael Maurer | Indoor/Outdoor Personal Security System |
JP2016514381A (en) | 2013-01-22 | 2016-05-19 | エデン ロック コミュニケーションズ, エルエルシーEden Rock Communications, Llc | Method and system for intelligent jamming signal generation |
US9167459B2 (en) | 2013-03-08 | 2015-10-20 | Litepoint Corporation | System and method for confirming radio frequency (RF) signal connection integrity with multiple devices under test (DUTs) to be tested concurrently |
JP6499154B2 (en) | 2013-03-11 | 2019-04-10 | マジック リープ, インコーポレイテッドMagic Leap,Inc. | Systems and methods for augmented and virtual reality |
US10257728B2 (en) | 2013-03-15 | 2019-04-09 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management |
US10231206B2 (en) | 2013-03-15 | 2019-03-12 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management for identifying signal-emitting devices |
US8750156B1 (en) | 2013-03-15 | 2014-06-10 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management for identifying open space |
US11646918B2 (en) | 2013-03-15 | 2023-05-09 | Digital Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management for identifying open space |
US9622041B2 (en) * | 2013-03-15 | 2017-04-11 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management |
US10299149B2 (en) | 2013-03-15 | 2019-05-21 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management |
US10219163B2 (en) | 2013-03-15 | 2019-02-26 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management |
US10257729B2 (en) | 2013-03-15 | 2019-04-09 | DGS Global Systems, Inc. | Systems, methods, and devices having databases for electronic spectrum management |
US8977212B2 (en) | 2013-03-15 | 2015-03-10 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management with remote access to data in a virtual computing network |
US10257727B2 (en) | 2013-03-15 | 2019-04-09 | DGS Global Systems, Inc. | Systems methods, and devices having databases and automated reports for electronic spectrum management |
US8798548B1 (en) | 2013-03-15 | 2014-08-05 | DGS Global Systems, Inc. | Systems, methods, and devices having databases for electronic spectrum management |
US9319916B2 (en) | 2013-03-15 | 2016-04-19 | Isco International, Llc | Method and appartus for signal interference processing |
US10237770B2 (en) | 2013-03-15 | 2019-03-19 | DGS Global Systems, Inc. | Systems, methods, and devices having databases and automated reports for electronic spectrum management |
US10271233B2 (en) | 2013-03-15 | 2019-04-23 | DGS Global Systems, Inc. | Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum |
US10244504B2 (en) | 2013-03-15 | 2019-03-26 | DGS Global Systems, Inc. | Systems, methods, and devices for geolocation with deployable large scale arrays |
US9537586B2 (en) | 2013-03-15 | 2017-01-03 | DGS Global Systems, Inc. | Systems, methods, and devices for electronic spectrum management with remote access to data in a virtual computing network |
US10122479B2 (en) | 2017-01-23 | 2018-11-06 | DGS Global Systems, Inc. | Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum |
US9572055B2 (en) | 2013-04-09 | 2017-02-14 | Spectrum Effect, Inc. | Uplink interference detection using transmission matrices |
US20140302796A1 (en) | 2013-04-09 | 2014-10-09 | Eden Rock Communications, Llc | Downlink interference detection using transmission matrices |
US9769834B2 (en) | 2013-05-07 | 2017-09-19 | Spectrum Effect, Inc. | Interference detection with UE signal subtraction |
EP2803975B1 (en) | 2013-05-17 | 2015-07-08 | Sick Ag | Method for laser spectroscopy of gases |
US9397761B2 (en) | 2013-05-17 | 2016-07-19 | Crfs Limited | RF signal generating device |
WO2015005954A1 (en) | 2013-07-11 | 2015-01-15 | Eden Rock Communications, Llc | Cooperative interference subtraction scheme |
GB2518010A (en) | 2013-09-09 | 2015-03-11 | Crfs Ltd | Frequency discriminator |
WO2015104059A1 (en) | 2014-01-10 | 2015-07-16 | Statoil Petroleum As | Determining a component of a wave field |
WO2015112197A1 (en) | 2014-01-27 | 2015-07-30 | Eden Rock Communications, Llc | Method and system for coexistence of radar and communication systems |
WO2015112200A1 (en) | 2014-01-27 | 2015-07-30 | Eden Rock Communications, Llc | Method and system for localizing interference in spectrum co-existence network |
WO2015161005A1 (en) | 2014-04-15 | 2015-10-22 | Eden Rock Communications, Llc | System and method for spectrum sharing |
US9275645B2 (en) | 2014-04-22 | 2016-03-01 | Droneshield, Llc | Drone detection and classification methods and apparatus |
US9143968B1 (en) | 2014-07-18 | 2015-09-22 | Cognitive Systems Corp. | Wireless spectrum monitoring and analysis |
US9942775B2 (en) | 2014-08-14 | 2018-04-10 | Spectrum Effect Inc. | Signal localization and mapping |
US10194324B2 (en) | 2014-08-14 | 2019-01-29 | Spectrum Effect Inc. | Carrier aggregation using shared spectrum |
US10144507B2 (en) * | 2014-08-28 | 2018-12-04 | Pascal Chretien | Electromagnetic distributed direct drive for aircraft |
GB2530272B (en) | 2014-09-16 | 2020-10-07 | Nottingham Scient Limited | GNSS Jamming Signal Detection |
JP6179000B2 (en) | 2014-10-27 | 2017-08-16 | エスゼット ディージェイアイ テクノロジー カンパニー リミテッドSz Dji Technology Co.,Ltd | Method, program and terminal for providing flight information |
US10338191B2 (en) | 2014-10-30 | 2019-07-02 | Bastille Networks, Inc. | Sensor mesh and signal transmission architectures for electromagnetic signature analysis |
US10264525B2 (en) | 2014-11-17 | 2019-04-16 | University Of Notre Dame Du Lac | Energy efficient communications |
US9689976B2 (en) * | 2014-12-19 | 2017-06-27 | Xidrone Systems, Inc. | Deterent for unmanned aerial systems |
US9715009B1 (en) | 2014-12-19 | 2017-07-25 | Xidrone Systems, Inc. | Deterent for unmanned aerial systems |
US9529360B1 (en) | 2015-01-28 | 2016-12-27 | Howard Melamed | System and method for detecting and defeating a drone |
US10129047B2 (en) | 2015-01-29 | 2018-11-13 | Time Warner Cable Enterprises Llc | Home automation system deployment |
GB2534894B (en) | 2015-02-02 | 2018-07-25 | Crfs Ltd | Direction finding using signal power |
CN107409051B (en) | 2015-03-31 | 2021-02-26 | 深圳市大疆创新科技有限公司 | Authentication system and method for generating flight controls |
US9767699B1 (en) | 2015-05-14 | 2017-09-19 | Rockwell Collins, Inc. | System for and method of detecting drones |
US9755972B1 (en) * | 2015-06-09 | 2017-09-05 | Google Inc. | Protocol-independent receive-side scaling |
FR3037659B1 (en) | 2015-06-17 | 2020-01-03 | Thales | METHOD FOR LOCATING AN ELECTROMAGNETIC TRANSMISSION SOURCE AND SYSTEM IMPLEMENTING SUCH A METHOD |
EP3329296B1 (en) | 2015-07-29 | 2021-09-15 | QUALCOMM Incorporated | Angular velocity sensing using arrays of antennas |
US10498955B2 (en) | 2015-08-03 | 2019-12-03 | Disney Enterprises, Inc. | Commercial drone detection |
US9933470B2 (en) | 2015-08-31 | 2018-04-03 | The Boeing Company | Energy spectrum visualization system |
CN107209854A (en) | 2015-09-15 | 2017-09-26 | 深圳市大疆创新科技有限公司 | For the support system and method that smoothly target is followed |
US9756423B2 (en) | 2015-09-16 | 2017-09-05 | Océ-Technologies B.V. | Method for removing electric crosstalk |
US10229329B2 (en) | 2016-11-08 | 2019-03-12 | Dedrone Holdings, Inc. | Systems, methods, apparatuses, and devices for identifying, tracking, and managing unmanned aerial vehicles |
KR102054089B1 (en) | 2015-09-28 | 2019-12-09 | 디파트먼트 13, 인코포레이티드 | Drone Intrusion Detection and Response Measures |
US9862489B1 (en) | 2016-02-07 | 2018-01-09 | Lee Weinstein | Method and apparatus for drone detection and disablement |
US10032464B2 (en) | 2015-11-24 | 2018-07-24 | Droneshield, Llc | Drone detection and classification with compensation for background clutter sources |
CN108603867B (en) | 2015-12-03 | 2020-11-06 | 株式会社岛津制作所 | Peak detection method and data processing apparatus |
US10241140B2 (en) | 2016-01-14 | 2019-03-26 | Syed Imran Mahmood Moinuddin | Systems and methods for monitoring power consumption |
CN108886374B (en) | 2016-01-18 | 2021-08-03 | 唯亚威通讯技术有限公司 | Method and apparatus for detecting distortion or deformation of cellular communication signals |
WO2017147781A1 (en) | 2016-03-01 | 2017-09-08 | 深圳市大疆创新科技有限公司 | Storage medium, unmanned aircraft, and shaking detection and tracking control method and system |
US20170261604A1 (en) | 2016-03-11 | 2017-09-14 | Raytheon Bbn Technologies Corp. | Intercept drone tasked to location of lidar tracked drone |
US9867080B2 (en) | 2016-03-31 | 2018-01-09 | T-Mobile Usa, Inc. | Determining network congestion based on target user throughput |
US10301041B2 (en) | 2016-06-09 | 2019-05-28 | California Institute Of Technology | Systems and methods for tracking moving objects |
US10157548B2 (en) | 2016-06-10 | 2018-12-18 | ETAK Systems, LLC | Waypoint directory in air traffic control systems for unmanned aerial vehicles |
US10411810B2 (en) | 2016-07-04 | 2019-09-10 | The Regents Of The University Of California | Receiver with mutually coherent optical frequency combs |
EP3494364A1 (en) * | 2016-08-05 | 2019-06-12 | Neu Robotics, Inc, | Mobile platform eg drone / uav performing localization and mapping using video |
US10389616B2 (en) | 2016-09-06 | 2019-08-20 | Spectrum Effect Inc. | Method and system for detecting interference to wireless networks |
JP6792148B2 (en) | 2016-09-21 | 2020-11-25 | 富士通株式会社 | Wireless analysis device and wireless analysis method |
US9805273B1 (en) | 2016-11-08 | 2017-10-31 | Dedrone Holdings, Inc. | Systems, methods, apparatuses, and devices for identifying and tracking unmanned aerial vehicles via a plurality of sensors |
GB2572722B (en) | 2017-01-10 | 2022-04-06 | Airshare Inc | System and method for intercepting unmanned aerial vehicles |
US10700794B2 (en) | 2017-01-23 | 2020-06-30 | Digital Global Systems, Inc. | Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time within an electromagnetic spectrum |
US10498951B2 (en) | 2017-01-23 | 2019-12-03 | Digital Global Systems, Inc. | Systems, methods, and devices for unmanned vehicle detection |
US10459020B2 (en) | 2017-01-23 | 2019-10-29 | DGS Global Systems, Inc. | Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time within a spectrum |
US10529241B2 (en) | 2017-01-23 | 2020-01-07 | Digital Global Systems, Inc. | Unmanned vehicle recognition and threat management |
US10187804B2 (en) | 2017-03-30 | 2019-01-22 | Intel Corporation | Allocating wireless channels |
US11029347B2 (en) | 2017-04-26 | 2021-06-08 | Nokomis, Inc | Electronics equipment testing apparatus and method utilizing unintended RF emission features |
CN107479368B (en) | 2017-06-30 | 2021-09-21 | 北京百度网讯科技有限公司 | Method and system for training unmanned aerial vehicle control model based on artificial intelligence |
US20190064223A1 (en) | 2017-08-25 | 2019-02-28 | Keysight Technologies, Inc. | Method and Apparatus for Detecting the Start of an Event in the Presence of Noise |
DK3461377T3 (en) | 2017-10-02 | 2021-02-15 | Rasmus Nilsson | THERAPEUTIC CARPET WITH WEIGHT ELEMENTS |
US10251242B1 (en) | 2017-10-04 | 2019-04-02 | Resilience Magnum IP, LLC | Information and hub lights |
US10868358B2 (en) * | 2017-10-19 | 2020-12-15 | Harris Solutions NY, Inc. | Antenna for wearable radio system and associated method of making |
US20190302249A1 (en) | 2018-03-29 | 2019-10-03 | Walmart Apollo, Llc | System and method for drone position determination |
US10784974B2 (en) | 2018-07-24 | 2020-09-22 | Spectrum Effect Inc. | Method and system for isolating related events in the presence of seasonal variations |
US10699585B2 (en) * | 2018-08-02 | 2020-06-30 | University Of North Dakota | Unmanned aerial system detection and mitigation |
US10917797B2 (en) | 2018-08-17 | 2021-02-09 | Spectrum Effect Inc. | Method and system for detecting and resolving anomalies in a wireless network |
US10594529B1 (en) | 2018-08-21 | 2020-03-17 | Bae Systems Information And Electronic Systems Integration Inc. | Variational design of companders for PAPR reduction in OFDM systems |
US10943461B2 (en) | 2018-08-24 | 2021-03-09 | Digital Global Systems, Inc. | Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time |
US10700721B2 (en) | 2018-11-02 | 2020-06-30 | Spectrum Effect Inc. | Remote passive intermodulation detection using nearby transmissions |
CN114598393A (en) * | 2020-12-07 | 2022-06-07 | 华为技术有限公司 | Signal processing method and device and communication system |
-
2013
- 2013-06-07 US US13/913,013 patent/US9622041B2/en active Active
- 2013-06-07 US US13/912,683 patent/US9288683B2/en active Active
- 2013-06-07 US US13/912,893 patent/US9078162B2/en active Active
- 2013-11-18 US US14/082,873 patent/US8805291B1/en active Active
- 2013-11-18 US US14/082,930 patent/US8824536B1/en active Active
- 2013-11-18 US US14/082,916 patent/US8780968B1/en active Active
-
2014
- 2014-05-08 US US14/273,193 patent/US8868004B2/en active Active
- 2014-07-07 US US14/325,044 patent/US8964824B2/en active Active
- 2014-07-11 US US14/329,815 patent/US8885696B1/en active Active
- 2014-10-02 US US14/504,743 patent/US9094974B2/en active Active
- 2014-10-02 US US14/504,770 patent/US9094975B2/en active Active
-
2015
- 2015-01-09 US US14/593,202 patent/US9749069B2/en active Active
- 2015-06-18 US US14/743,011 patent/US9414237B2/en active Active
- 2015-07-01 US US14/788,838 patent/US9253648B2/en active Active
- 2015-07-01 US US14/788,842 patent/US9420473B2/en active Active
- 2015-12-30 US US14/983,690 patent/US20160119794A1/en not_active Abandoned
- 2015-12-30 US US14/983,678 patent/US20160119806A1/en not_active Abandoned
-
2016
- 2016-08-04 US US15/228,325 patent/US9998243B2/en active Active
- 2016-08-15 US US15/236,524 patent/US20160374088A1/en not_active Abandoned
-
2018
- 2018-06-07 US US16/002,751 patent/US10284309B2/en active Active
-
2019
- 2019-04-26 US US16/395,987 patent/US10554317B2/en active Active
-
2020
- 2020-01-24 US US16/751,887 patent/US11223431B2/en active Active
-
2022
- 2022-01-06 US US17/569,984 patent/US11588562B2/en active Active
- 2022-12-15 US US18/082,210 patent/US11637641B1/en active Active
-
2023
- 2023-04-21 US US18/137,785 patent/US11791913B2/en active Active
- 2023-09-28 US US18/374,385 patent/US11901963B1/en active Active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140064723A1 (en) * | 2012-05-01 | 2014-03-06 | The Johns Hopkins University | Cueing System for Universal Optical Receiver |
Non-Patent Citations (1)
Title |
---|
David Eppink and Wolf Kuebler, "TIREM/SEM HANDBOOK", March 1994, llT Research Institute, p. 1-6, located at http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA296913 * |
Cited By (29)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10623215B2 (en) | 2013-11-18 | 2020-04-14 | Bae Systems Information And Electronic Systems Integration Inc. | Process for tunnelized cyclostationary to achieve low-energy spectrum sensing |
US20170303014A1 (en) * | 2016-04-18 | 2017-10-19 | Karim Ghessassi | System for providing functionality based on sensor data |
US11516763B2 (en) * | 2016-06-30 | 2022-11-29 | HawkEye 360, Inc. | Determining emitter locations |
US9661604B1 (en) * | 2016-06-30 | 2017-05-23 | HawkEye 360, Inc. | Determining emitter locations |
US11882540B2 (en) | 2016-06-30 | 2024-01-23 | HawkEye 360, Inc. | Determining emitter locations |
US10057873B2 (en) * | 2016-06-30 | 2018-08-21 | HawkEye 360, Inc. | Determining emitter locations |
US20190037520A1 (en) * | 2016-06-30 | 2019-01-31 | HawkEye 360, Inc. | Determining emitter locations |
US10440677B2 (en) * | 2016-06-30 | 2019-10-08 | HawkEye 360, Inc. | Determining emitter locations |
US10813073B2 (en) * | 2016-06-30 | 2020-10-20 | HawkEye 360, Inc. | Determining emitter locations |
US20190380105A1 (en) * | 2016-06-30 | 2019-12-12 | HawkEye 360, Inc. | Determining emitter locations |
US20180007653A1 (en) * | 2016-06-30 | 2018-01-04 | HawkEye 360, Inc. | Determining emitter locations |
US10666473B2 (en) | 2016-09-27 | 2020-05-26 | Bae Systems Information And Electronic Systems Integration Inc. | Techniques for implementing a portable spectrum analyzer |
US10313164B2 (en) * | 2016-09-27 | 2019-06-04 | Bae Systems Information And Electronic Systems Integration Inc. | Techniques for implementing a portable spectrum analyzer |
US20190215248A1 (en) * | 2017-06-28 | 2019-07-11 | Ciena Corporation | Multi-layer optical network management graphical user interface and visualizations |
US11894987B2 (en) | 2017-06-28 | 2024-02-06 | Ciena Corporation | Multi-layer optical network management graphical user interface and visualizations |
US11463325B2 (en) * | 2017-06-28 | 2022-10-04 | Ciena Corporation | Multi-layer optical network management graphical user interface and visualizations |
US10466336B2 (en) | 2017-06-30 | 2019-11-05 | HawkEye 360, Inc. | Detecting radio signal emitter locations |
US10859668B2 (en) | 2017-06-30 | 2020-12-08 | HawkEye 360, Inc. | Detecting radio signal emitter locations |
US11480649B2 (en) | 2017-06-30 | 2022-10-25 | HawkEye 360. Inc. | Detecting radio signal emitter locations |
US11711709B2 (en) | 2018-08-23 | 2023-07-25 | Tracfone Wireless, Inc. | System and process for using cellular connectivity analysis to determine optimal wireless equipment and service for a geographical area |
US11237277B2 (en) | 2019-02-15 | 2022-02-01 | Horizon Technologies Consultants, Ltd. | Techniques for determining geolocations |
US11821997B2 (en) | 2019-02-15 | 2023-11-21 | Horizon Technologies Consultants, Ltd. | Techniques for determining geolocations |
US20220077941A1 (en) * | 2019-05-15 | 2022-03-10 | Astrapi Corporation | Frequency spectrum analyzers and devices, systems, software and methods for signal power measurement and spectrum analysis |
US20200389240A1 (en) * | 2019-06-04 | 2020-12-10 | Thayermahan, Inc. | Portable sensor fusion broadcast system for maritime situational awareness |
US11664911B2 (en) * | 2019-06-04 | 2023-05-30 | Thayermahan, Inc. | Portable sensor fusion broadcast system for maritime situational awareness |
US10912003B1 (en) * | 2019-09-27 | 2021-02-02 | Fortinet, Inc. | Spectral efficient selection of station clusters for concurrent data transmissions in high efficiency WLANs (wireless local access networks) using unsupervised machine learning models |
US11101884B1 (en) | 2020-03-24 | 2021-08-24 | Ciena Corporation | Localizing anomalies of a fiber optic network within the context of a geographic map |
US11563486B2 (en) | 2020-03-24 | 2023-01-24 | Ciena Corporation | Logical to physical link mapping in a fiber optic network |
US11523370B1 (en) * | 2020-05-22 | 2022-12-06 | Bae Systems Information And Electronic Systems Integration Inc. | Efficient graphics processing unit (GPU) pulse detector |
Also Published As
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11509512B2 (en) | Systems, methods, and devices for electronic spectrum management for identifying open space | |
US9008587B2 (en) | Systems, methods, and devices having databases for electronic spectrum management | |
US20160119806A1 (en) | Systems, methods, and devices having databases and automated reports for electronic spectrum management | |
US9537586B2 (en) | Systems, methods, and devices for electronic spectrum management with remote access to data in a virtual computing network | |
US9185591B2 (en) | Systems, methods, and devices for electronic spectrum management with remote access to data in a virtual computing network | |
US9253673B2 (en) | Systems, methods, and devices having databases and automated reports for electronic spectrum management | |
US11646918B2 (en) | Systems, methods, and devices for electronic spectrum management for identifying open space | |
US9191848B2 (en) | Systems, methods, and devices for electronic spectrum management for identifying signal-emitting devices | |
US20160080955A1 (en) | Systems, methods, and devices for electronic spectrum management with remote access to data in a virtual computing network | |
WO2014144831A1 (en) | Systems, methods, and devices having databases for electronic spectrum management | |
US20160323920A1 (en) | Systems, methods, and devices having databases for electronic spectrum management | |
US20160072597A1 (en) | Systems, Methods, and Devices for Electronic Spectrum Management for Identifying Signal-Emitting Devices | |
WO2014144838A1 (en) | Systems, methods, and devices for electronic spectrum management |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: DGS GLOBAL SYSTEMS, INC., MARYLAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:CARBAJAL, DANIEL;REEL/FRAME:037381/0020 Effective date: 20131113 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |
|
AS | Assignment |
Owner name: DIGITAL GLOBAL SYSTEMS, INC., MARYLAND Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE RECEIVING PARTY NAME PREVIOUSLY RECORDED AT REEL: 37381 FRAME: 020. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT;ASSIGNOR:CARBAJAL, DANIEL;REEL/FRAME:049589/0741 Effective date: 20131113 |