CN104035066A - Target motion and rest state judging method based on passive multi-point positioning technology - Google Patents

Abstract

The invention discloses a target motion and rest state judging method based on the passive multi-point positioning technology. The method can perform judgment through a signal measured value or multiple measured values. The judgment step with the signal measured value comprises computing the geometric accuracy factor matrix of the single measured value and utilizing the geometric accuracy factor matrix to normalize the measured value to obtain a judgment statistic. The judgment step with the multiple measured values comprises subtracting the N-1th measured value from the Nth measured value, composing a column vector according to x-axis data and y-axis data, structuring an associated matrix of the vector by combining with the geometric accuracy factor matrix computed from the measured values to normalize the column vector and further to structure a judgment statistic, and performing judgment according to a threshold value. The target motion and rest state judging method based on the passive multi-point positioning technology is based on statistical hypothesis testing and combined with an airport surface monitoring multi-point location system, thereby being high in system flexibility and robustness, small in calculating amount, good in detecting performance and capable of performing judgment flexibly through the single measured value and the multiple measured values.

Description

Target travel-stationary state determination methods based on passive multipoint positioning technology

Technical field

The present invention relates to the targeted surveillance technical field in civil aviation field, particularly a kind of target travel-stationary state determination methods based on passive multipoint positioning technology.

Background technology

The supervision of airdrome scene target is the hot issue in civil aviation field.Traditional scene monitoring system is surveillance radar of scene, and this equipment exists some shortcomings, and as low in scene monitoring precision, monitoring range is subject to airport environment to affect greatly, be subject to the climate effects such as sleet sky.

Airport scene monitoring multipoint location system belongs to the passive location system based on time difference location, and its principle is first to obtain the time of arrival poor (TDOA) that is distributed in diverse location receiving station institute receiving target, then calculates coordinates of targets by location algorithm.

Measured data shows, the target location that airport scene monitoring multipoint location system provides (below all referred to as measuring value) has following features:

1, before and after same target, the time interval between continuous quantity measured value is irregular, its scope by Microsecond grade to tens of seconds levels;

2, the statistical property of airport scene monitoring measurement noise that multipoint location system is introduced is relevant with scene position, target place; Meanwhile, between the measurement noise in different coordinate axis, there is statistic correlation, and its correlativity is also relevant with scene position, target place.This feature is to be determined by airport scene monitoring multipoint location system inherent characteristic.

In the actual use of airport scene monitoring multipoint location system, there is a kind of very real new demand, to judge motion or the stationary state of target, by the judgement of this state, not only can demonstrate target kinetic characteristic more accurately, and contribute to more effectively to remove the open country value that sighting distance indirect wave signal causes.

For the measuring value feature of above-mentioned airport scene monitoring multipoint location system and new demand, the target travel-stationary state determination methods based on passive multipoint positioning technology involved in the present invention is just arisen at the historic moment.

Summary of the invention

Goal of the invention of the present invention is: for the problem of above-mentioned existence, provide a kind of target travel-stationary state determination methods based on passive multipoint positioning technology.

Method involved in the present invention is applied to a submodule of tracking filter part in airport scene monitoring multipoint location system, take assumed statistical inspection as theoretical foundation and combine the feature of airport scene monitoring multipoint location system, can judge reliably the current motion conditions of target.In reality, judgement data are the target localization position that airport scene monitoring multipoint location system calculates, and are output as judged result (motion or stationary state).

The two dimensional surface rectangular coordinate of take carries out following derivation as example:

Aim parameter measured value can be expressed as:

$\left\{\begin{array}{c}{x}_k=x_k^0+n_{\mathrm{xk}}\\ y_k=y_k^0+n_{\mathrm{yk}}\end{array}\right.---\left(1\right)$

X _k, y _kfor target k measuring value constantly, wherein for target k actual position constantly, n _xk, n _ykbe respectively the k noise (be zero-mean white Gaussian noise stochastic process) of measuring value on x axle, y axle constantly.

K measuring value is constantly deducted to k-1 measuring value constantly, on x axle, y axle, use respectively △ x _k, △ y _krepresent.As use H ₀represent target stationary state hypothesis, H ₁represent target state hypothesis, have

V wherein _xk, v _ykfor k-1 is to k moment target velocity, t _kthe time of arrival (TOA) that represents k moment measuring value, symbol &, | difference presentation logic " with " (satisfying condition) and "or" (meeting one of them condition) simultaneously.

Obviously, (v when target travel _xk≠ 0 or v _yk≠ 0), the judgement decision problem of above-mentioned Stillness and motion is converted into judgement △ x _k, △ y _kzero-mean Gaussian random variable or the decision problem of Non-zero Mean Gaussian random variable.

The invention provides two target travel-stationary state determination methods based on passive multipoint positioning technology, a judgement that is applicable to a plurality of measuring values, another is applicable to the judgement of single measuring value, and particular content is as follows.

The determination methods that is applicable to a plurality of measuring values is specially:

Target travel-stationary state determination methods based on passive multipoint positioning technology, is characterized in that comprising the following steps:

The first step, from airport scene monitoring multipoint location system, obtain N target measuring value as a window treatments sample: { x ₁, y ₁, { x ₂, y ₂... { x _n, y _n, calculate respectively the geometric dilution of precision matrix of this N measuring value, ${\mathrm{\φ}}_i=\left[\begin{array}{cc}E\left(x_i^2\right)& E\left(x_i{y}_i\right)\\ E\left(x_i{y}_i\right)& E\left(y_i^2\right)\end{array}\right],$ I=1～N, wherein represent mathematical expectation;

Second step, N measuring value of use deduct respectively a remaining N-1 measuring value, obtain △ x _ni=x _n-x _i, i=1～N-1, △ y _ni=y _n-y _i, i=1～N-1;

The 3rd step, structure ${\mathrm{\Δ}}_x=\left[\begin{array}{c}{\mathrm{\Δx}}_{N1}\\ {\mathrm{\Δx}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δx}}_{\mathrm{NN}-1}\end{array}\right]$ Correlation matrix:

$Q_{\mathrm{xx}}=E\left\{{\mathrm{\Δ}}_x{\mathrm{\Δ}}_x^T\right\}=\left[\begin{array}{c}E\left(x_N^2\right)-E\left(x_1^2\right),E\left(x_N^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)\\ E\left(x_N^2\right),E\left(x_N^2\right)-E\left(x_2^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)\\ \·\·\·\\ E\left(x_N^2\right),E\left(x_N^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)-E\left(x_{N-1}^2\right)\end{array}\right],$

Build ${\mathrm{\Δ}}_y=\left[\begin{array}{c}{\mathrm{\Δy}}_{N1}\\ {\mathrm{\Δy}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δy}}_{\mathrm{NN}-1}\end{array}\right]$ Correlation matrix:

$Q_{\mathrm{yy}}=E\left\{{\mathrm{\Δ}}_y{\mathrm{\Δ}}_y^T\right\}=\left[\begin{array}{c}E\left(y_N^2\right)-E\left(y_1^2\right),E\left(y_N^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)\\ E\left(y_N^2\right),E\left(y_N^2\right)-E\left(y_2^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)\\ \·\·\·\\ E\left(y_N^2\right),E\left(y_N^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)-E\left(y_{N-1}^2\right)\end{array}\right],$

Build ${\mathrm{\Δ}}_x=\left[\begin{array}{c}{\mathrm{\Δx}}_{N1}\\ {\mathrm{\Δx}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δx}}_{\mathrm{NN}-1}\end{array}\right]$ And ${\mathrm{\Δ}}_y=\left[\begin{array}{c}{\mathrm{\Δy}}_{N1}\\ {\mathrm{\Δy}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δy}}_{\mathrm{NN}-1}\end{array}\right]$ Cross-correlation matrix:

$Q_{\mathrm{xy}}=E\left\{{\mathrm{\Δ}}_x{\mathrm{\Δ}}_y^T\right\}=\left[\begin{array}{c}E\left(x_N{y}_N\right)-E\left(x_1{y}_1\right),E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)\\ E\left(x_N{y}_N\right),E\left(x_N{y}_N\right)-E\left(x_2{y}_2\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)\\ \·\·\·\\ E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)-E\left(x_{N-1}{y}_{N-1}\right)\end{array}\right];$

The 4th step, by the synthetic Matrix C of 3 matrix group of the 3rd step gained _xy, $C_{\mathrm{xy}}=\left[\begin{array}{cc}{Q}_{\mathrm{xx}}& Q_{\mathrm{xy}}\\ Q_{\mathrm{xy}}& Q_{\mathrm{yy}}\end{array}\right],$ By △ _xand △ _ybe combined into a column vector Z _xy, $Z_{\mathrm{xy}}=\left[\begin{array}{c}{\mathrm{\Δ}}_x\\ {\mathrm{\Δ}}_y\end{array}\right];$

The 5th step, calculating ${\mathrm{\ξ}}_w=Z_{\mathrm{xy}}^T{C}_{\mathrm{xy}}^{-1}{Z}_{\mathrm{xy}};$

The 6th step, setting level of significance α _w, can determine measuring value thresholding M _w, re-use ξ _wmake a decision: work as ξ _w>M _wtime, object judgement is motion; Work as ξ _w≤ M _wtime, judgement target is static;

The 7th step, output judged result;

After the 8th step, measuring value of window sliding, repeat again above steps.

Said method is applicable to a plurality of measuring values to slide window test of hypothesis, and calculated amount is larger, but detecting performance increases.

The determination methods that is applicable to single measuring value is specially:

Target travel-stationary state determination methods based on passive multipoint positioning technology, is characterized in that comprising the following steps:

The first step, from airport scene monitoring multipoint location system amount to obtain measured value x _k, y _k;

Second step, computational geometry dilution of precision matrix, ${\mathrm{\φ}}_k=\left[\begin{array}{cc}E\left(x_k^2\right)& E\left(x_k{y}_k\right)\\ E\left(x_k{y}_k\right)& E\left(y_k^2\right)\end{array}\right],$ Wherein represent mathematical expectation;

The 3rd step, calculating ${\mathrm{\ξ}}_k={\left[\begin{array}{cc}{\mathrm{\Δx}}_k& {\mathrm{\Δy}}_k\end{array}\right]\mathrm{\φ}}_k^{-1}\left[\begin{array}{c}{\mathrm{\Δx}}_k\\ {\mathrm{\Δy}}_k\end{array}\right],$ If target is stationary state, ξ k obeys the χ that degree of freedom is 2 ²distribute; If target is motion state, i.e. △ x _kor △ y _kno longer zero-mean Gaussian random variable, ξ _kwill significantly become large;

The 4th step, set level of significance α, can determine the thresholding M of single measuring value, re-use ξ _kdo as judged:

A, work as ξ _kduring >M, object judgement is motion;

B, work as ξ _kduring≤M, judgement target is static

The 5th step, output judged result.

Said method is applicable to single measuring value to carry out assumed statistical inspection, and computing velocity is very fast, have good real-time, but false alarm rate is higher, is easy to judge by accident.

The determination methods that is applicable to a plurality of measuring values is to put forward on the basis of single measuring value determination methods, and principle is similar, just in calculated amount and detection performance, has difference, in reality, can require to adopt any method according to concrete engineering.

In sum, owing to having adopted technique scheme, the invention has the beneficial effects as follows: two methods are the principle based on assumed statistical inspection all, and in conjunction with the feature of airport scene monitoring multipoint location system, there is stronger system flexibility and robustness; In actual computation process, the value of i=1～N is all very approaching, only need to calculate one, thereby can improve counting yield; Two methods respectively possess some good points, can be according to the flexible choice for use of actual conditions.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of the embodiment of the present invention 1.

Fig. 2 is the process flow diagram of the embodiment of the present invention 2.

Embodiment

Below in conjunction with accompanying drawing, the present invention is described in detail.

Embodiment 1:

As shown in Figure 1, a kind of target travel-stationary state determination methods based on passive multipoint positioning technology, is characterized in that comprising the following steps:

The first step, from airport scene monitoring multipoint location system, obtain N target measuring value as a window treatments sample: { x ₁, y ₁, { x ₂, y ₂... { x _n, y _n, calculate respectively the geometric dilution of precision matrix of this N measuring value, ${\mathrm{\φ}}_i=\left[\begin{array}{cc}E\left(x_i^2\right)& E\left(x_i{y}_i\right)\\ E\left(x_i{y}_i\right)& E\left(y_i^2\right)\end{array}\right],$ I=1～N, wherein represent mathematical expectation;

Second step, N measuring value of use deduct respectively a remaining N-1 measuring value, obtain △ x _ni=x _n-x _i, i=1～N-1, △ y _ni=y _n-y _i, i=1～N-1;

The 3rd step, structure ${\mathrm{\Δ}}_x=\left[\begin{array}{c}{\mathrm{\Δx}}_{N1}\\ {\mathrm{\Δx}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δx}}_{\mathrm{NN}-1}\end{array}\right]$ Correlation matrix

$Q_{\mathrm{xx}}=E\left\{{\mathrm{\Δ}}_x{\mathrm{\Δ}}_x^T\right\}=\left[\begin{array}{c}E\left(x_N^2\right)-E\left(x_1^2\right),E\left(x_N^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)\\ E\left(x_N^2\right),E\left(x_N^2\right)-E\left(x_2^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)\\ \·\·\·\\ E\left(x_N^2\right),E\left(x_N^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)-E\left(x_{N-1}^2\right)\end{array}\right],$

Build ${\mathrm{\Δ}}_y=\left[\begin{array}{c}{\mathrm{\Δy}}_{N1}\\ {\mathrm{\Δy}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δy}}_{\mathrm{NN}-1}\end{array}\right]$ Correlation matrix

$Q_{\mathrm{yy}}=E\left\{{\mathrm{\Δ}}_y{\mathrm{\Δ}}_y^T\right\}=\left[\begin{array}{c}E\left(y_N^2\right)-E\left(y_1^2\right),E\left(y_N^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)\\ E\left(y_N^2\right),E\left(y_N^2\right)-E\left(y_2^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)\\ \·\·\·\\ E\left(y_N^2\right),E\left(y_N^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)-E\left(y_{N-1}^2\right)\end{array}\right],$

Build ${\mathrm{\Δ}}_x=\left[\begin{array}{c}{\mathrm{\Δx}}_{N1}\\ {\mathrm{\Δx}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δx}}_{\mathrm{NN}-1}\end{array}\right]$ And ${\mathrm{\Δ}}_y=\left[\begin{array}{c}{\mathrm{\Δy}}_{N1}\\ {\mathrm{\Δy}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δy}}_{\mathrm{NN}-1}\end{array}\right]$ Cross-correlation matrix

$Q_{\mathrm{xy}}=E\left\{{\mathrm{\Δ}}_x{\mathrm{\Δ}}_y^T\right\}=\left[\begin{array}{c}E\left(x_N{y}_N\right)-E\left(x_1{y}_1\right),E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)\\ E\left(x_N{y}_N\right),E\left(x_N{y}_N\right)-E\left(x_2{y}_2\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)\\ \·\·\·\\ E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)-E\left(x_{N-1}{y}_{N-1}\right)\end{array}\right];$

The 4th step, by the synthetic Matrix C of 3 matrix group of the 3rd step gained _xy, $C_{\mathrm{xy}}=\left[\begin{array}{cc}{Q}_{\mathrm{xx}}& Q_{\mathrm{xy}}\\ Q_{\mathrm{xy}}& Q_{\mathrm{yy}}\end{array}\right],$ By △ _xand △ _ybe combined into a column vector Z _xy, $Z_{\mathrm{xy}}=\left[\begin{array}{c}{\mathrm{\Δ}}_x\\ {\mathrm{\Δ}}_y\end{array}\right];$

The 5th step, calculating ${\mathrm{\ξ}}_w=Z_{\mathrm{xy}}^T{C}_{\mathrm{xy}}^{-1}{Z}_{\mathrm{xy}};$

The 6th step, setting level of significance α _w, can determine measuring value thresholding M _w, re-use ξ _wmake a decision: work as ξ _w>M _wtime, object judgement is motion; Work as ξ _w≤ M _wtime, judgement target is static;

The 7th step, output judged result;

After the 8th step, measuring value of window sliding, repeat again above steps.

Embodiment 2:

As shown in Figure 2, a kind of target travel-stationary state determination methods based on passive multipoint positioning technology, is characterized in that comprising the following steps:

The first step, from airport scene monitoring multipoint location system amount to obtain measured value x _k, y _k;

Second step, computational geometry dilution of precision matrix, ${\mathrm{\φ}}_k=\left[\begin{array}{cc}E\left(x_k^2\right)& E\left(x_k{y}_k\right)\\ E\left(x_k{y}_k\right)& E\left(y_k^2\right)\end{array}\right],$ Wherein represent mathematical expectation;

The 3rd step, calculating ${\mathrm{\ξ}}_k={\left[\begin{array}{cc}{\mathrm{\Δx}}_k& {\mathrm{\Δy}}_k\end{array}\right]\mathrm{\φ}}_k^{-1}\left[\begin{array}{c}{\mathrm{\Δx}}_k\\ {\mathrm{\Δy}}_k\end{array}\right],$ If target is stationary state, ξ _kthe χ that obedience degree of freedom is 2 ²distribute; If target is motion state, i.e. △ x _kor △ y _kno longer zero-mean Gaussian random variable, ξ _kwill significantly become large;

The 4th step, set level of significance α, can determine the thresholding M of single measuring value, re-use ξ _kdo as judged: a, work as ξ _kduring >M, object judgement is motion; B, work as ξ _kduring≤M, judgement target is static

The 5th step, output judged result.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.

Claims (2)

1. target travel-stationary state the determination methods based on passive multipoint positioning technology, is characterized in that comprising the following steps:

The first step, from airport scene monitoring multipoint location system, obtain target N measuring value as the processing sample in a window: { x ₁, y ₁, { x ₂, y ₂... { x _n, y _n, calculate respectively the geometric dilution of precision matrix of this N measuring value, ${\mathrm{\φ}}_i=\left[\begin{array}{cc}E\left(x_i^2\right)& E\left(x_i{y}_i\right)\\ E\left(x_i{y}_i\right)& E\left(y_i^2\right)\end{array}\right],$ I=1～N, wherein represent mathematical expectation;

Second step, N measuring value of use deduct respectively a remaining N-1 measuring value, obtain △ x _ni=x _n-x _i, i=1～N-1, △ y _ni=y _n-y _i, i=1～N-1;

The 3rd step, structure vector ${\mathrm{\Δ}}_x=\left[\begin{array}{c}{\mathrm{\Δx}}_{N1}\\ {\mathrm{\Δx}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δx}}_{\mathrm{NN}-1}\end{array}\right],$ And calculate △ according to the geometric dilution of precision matrix of calculated a N measuring value _xcorrelation matrix:

$Q_{\mathrm{xx}}=E\left\{{\mathrm{\Δ}}_x{\mathrm{\Δ}}_x^T\right\}=\left[\begin{array}{c}E\left(x_N^2\right)-E\left(x_1^2\right),E\left(x_N^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)\\ E\left(x_N^2\right),E\left(x_N^2\right)-E\left(x_2^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)\\ \·\·\·\\ E\left(x_N^2\right),E\left(x_N^2\right),E\left(x_N^2\right),\·\·\·E\left(x_N^2\right)-E\left(x_{N-1}^2\right)\end{array}\right],$

Build vector ${\mathrm{\Δ}}_y=\left[\begin{array}{c}{\mathrm{\Δy}}_{N1}\\ {\mathrm{\Δy}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δy}}_{\mathrm{NN}-1}\end{array}\right],$ And calculate △ according to the geometric dilution of precision matrix of calculated a N measuring value _ycorrelation matrix:

$Q_{\mathrm{yy}}=E\left\{{\mathrm{\Δ}}_y{\mathrm{\Δ}}_y^T\right\}=\left[\begin{array}{c}E\left(y_N^2\right)-E\left(y_1^2\right),E\left(y_N^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)\\ E\left(y_N^2\right),E\left(y_N^2\right)-E\left(y_2^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)\\ \·\·\·\\ E\left(y_N^2\right),E\left(y_N^2\right),E\left(y_N^2\right),\·\·\·E\left(y_N^2\right)-E\left(y_{N-1}^2\right)\end{array}\right],$

According to the geometric dilution of precision matrix of calculated a N measuring value, calculate ${\mathrm{\Δ}}_x=\left[\begin{array}{c}{\mathrm{\Δx}}_{N1}\\ {\mathrm{\Δx}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δx}}_{\mathrm{NN}-1}\end{array}\right]$ And ${\mathrm{\Δ}}_y=\left[\begin{array}{c}{\mathrm{\Δy}}_{N1}\\ {\mathrm{\Δy}}_{N2}\\ \·\\ \·\\ \·\\ {\mathrm{\Δy}}_{\mathrm{NN}-1}\end{array}\right]$ Cross-correlation matrix

$Q_{\mathrm{xy}}=E\left\{{\mathrm{\Δ}}_x{\mathrm{\Δ}}_y^T\right\}=\left[\begin{array}{c}E\left(x_N{y}_N\right)-E\left(x_1{y}_1\right),E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)\\ E\left(x_N{y}_N\right),E\left(x_N{y}_N\right)-E\left(x_2{y}_2\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)\\ \·\·\·\\ E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),E\left(x_N{y}_N\right),\·\·\·E\left(x_N{y}_N\right)-E\left(x_{N-1}{y}_{N-1}\right)\end{array}\right];$

The 4th step, by the synthetic Matrix C of 3 matrix group of the 3rd step gained _xy, $C_{\mathrm{xy}}=\left[\begin{array}{cc}{Q}_{\mathrm{xx}}& Q_{\mathrm{xy}}\\ Q_{\mathrm{xy}}& Q_{\mathrm{yy}}\end{array}\right],$ Again by △ _xand △ _ybe combined into a column vector Z _xy, $Z_{\mathrm{xy}}=\left[\begin{array}{c}{\mathrm{\Δ}}_x\\ {\mathrm{\Δ}}_y\end{array}\right];$

The 5th step, calculating ${\mathrm{\ξ}}_w=Z_{\mathrm{xy}}^T{C}_{\mathrm{xy}}^{-1}{Z}_{\mathrm{xy}};$

The 6th step, setting level of significance α _w, can determine measuring value thresholding M _w, re-use ξ _wmake a decision:

A, work as ξ _w>M _wtime, object judgement is motion;

B, work as ξ _w≤ M _wtime, judgement target is static;

The 7th step, output judged result;

After the 8th step, measuring value of window sliding, repeat again above steps.

2. target travel-stationary state the determination methods based on passive multipoint positioning technology, is characterized in that comprising the following steps:

The first step, from airport scene monitoring multipoint location system amount to obtain measured value x _k, _yk;

Second step, computational geometry dilution of precision matrix, ${\mathrm{\φ}}_k=\left[\begin{array}{cc}E\left(x_k^2\right)& E\left(x_k{y}_k\right)\\ E\left(x_k{y}_k\right)& E\left(y_k^2\right)\end{array}\right],$ Wherein E (xi2), represent mathematical expectation;

The 3rd step, calculating ${\mathrm{\ξ}}_k={\left[\begin{array}{cc}{\mathrm{\Δx}}_k& {\mathrm{\Δy}}_k\end{array}\right]\mathrm{\φ}}_k^{-1}\left[\begin{array}{c}{\mathrm{\Δx}}_k\\ {\mathrm{\Δy}}_k\end{array}\right],$ If target is stationary state, ξ _kthe χ that obedience degree of freedom is 2 ²distribute; If target is motion state, i.e. △ x _kor △ y _kno longer zero-mean Gaussian random variable, ξ _kwill significantly become large;

The 4th step, set level of significance α, can determine the thresholding M of single measuring value, re-use ξ _kdo as judged:

A, work as ξ _kduring >M, object judgement is motion;

B, work as ξ _kduring≤M, judgement target is static;

The 5th step, output judged result.