CN103728599A - Method for restraining deception false target jamming through remotely configured initiative and passive radar net - Google Patents

Abstract

The invention discloses a method for restraining deception false target jamming through a remotely configured initiative and passive radar net. The method for restraining the deception false target jamming through the remotely configured initiative and passive radar net mainly solves the problem that the prior art is high in false target error identification probability. The method for restraining the deception false target jamming through the remotely configured initiative and passive radar net comprises steps of 1, detecting a target through an initiative radar and a passive radar in the initiative and passive radar net and obtaining a measuring value of the target; 2, computing an estimated value and an error covariance matrix of a measuring value of the passive radar through a measuring value of the initiative radar; 3, computing the correlation distance of all targets of the initiative radar and the passive radar at different moments according to the estimated value and a target measuring value of the measuring value of the passive radar and accordingly obtaining identification statistics; 4, setting a detection threshold, comparing the identification statistics with the threshold and obtaining a correlation detection result; 5, performing ambiguity processing on the correlation detection result, performing true and false judgment on all the targets of the initiative radar through the processed result and removing false targets. The method for restraining the deception false target jamming through the remotely configured initiative and passive radar net reduces the error identification probability of the false targets and effectively achieves restraining of the deception false targets.

Description

With the active radar and passive radar net of strange land configuration, suppress the method that false targets disturbs

Technical field

The present invention relates to Radar Technology field, be particularly related to deception formula and disturb countermeasure techniques, specifically utilize the active radar and passive radar net of strange land configuration to suppress the method that false targets disturbs, the method can be used for the data fusion center of active radar and passive radar net, by a mark Testing Association, differentiate and reject false targets and disturb, improving the detecting and tracking performance of active radar and passive radar net under false targets disturbs.

Background technology

The active interference that jammer can carry out radar is divided into pressing type interference and deception formula is disturbed, and because deception formula is disturbed the pulse compression gain that can utilize radar receiver, therefore jamming effectiveness is higher, thereby is subject to extensive concern.Especially the appearance of digital radiofrequency memory, make active cheating formula EVAC (Evacuation Network Computer Model) quickly and accurately the radar signal of intercepting and capturing to be stored, to be modulated and to forward, thereby produce a large amount of false targets at true target proximity, take a large amount of system resource, have a strong impact on detection and the tracking performance of radar system.

For false targets, disturb, monostatic radar is because visual angle is single, be difficult to it to resist, and the method for radar network utilisation point mark association is carried out true and false differentiation to the target detecting, and reject decoy, thereby realize the antagonism that deception formula is disturbed, but, in radar network, each node radar all can be subject to the interference of deception formula, the wrong association probability that intensive decoy can cause carrying out between the measuring value of each node radar Testing Association is higher, and radar network cloth station location is undesirable, also can reduce the ability that radar network antagonism deception formula is disturbed.

For the limited problem of the anti-false targets interference performance of radar network, a kind of method of utilizing the anti-false targets interference of active radar and passive radar net of strange land configuration of the propositions such as Li Shizhong, is shown in that the < < firepower in May, 2013 and commander control 10th～13 pages of > the 38th the 5th phases of volume of >.Active radar and passive radar net is to consist of the Active Radar and the passive radar networking that are distributed in diverse location, by data fusion center, carried out a radar fence of unified management, passive radar is due to not outside emittance, jammer is operated under passive mode, so cannot disturb to its enforcement deception formula by intercepting and capturing its transmitting information.The method that Li Shizhong proposes is exactly the feature of having utilized active radar and passive radar net, and the measuring value detecting according to passive radar carries out true and false judgement to the target in Active Radar, to differentiate and to reject the decoy in Active Radar.But the method is carried out a some mark when associated again, has only utilized the azimuth information in passive radar measurement information, does not utilize angle of pitch information, higher to the mistake discrimination probability of decoy.

Summary of the invention

The object of the invention is to for the above-mentioned existing method problem higher to the mistake discrimination probability of false targets, propose a kind of active radar and passive radar net with strange land configuration and suppress the method that false targets disturbs, effectively to reduce the mistake discrimination probability of active radar and passive radar net to decoy.

For achieving the above object, technical scheme of the present invention comprises the steps:

(1) with the Active Radar in active radar and passive radar net, target is detected, obtain not measuring value Z in the same time of each target of Active Radar _i(k):

Wherein, i represents the sequence number of target in Active Radar, i=1, and 2 ..., M, M is the number that Active Radar detects target, k=1,2 ..., T, T is active radar and passive radar net total length detection time, r _i(k), θ _i(k),

be respectively i target at k constantly with respect to radial distance, position angle and the angle of pitch of Active Radar;

(2) with the passive radar in active radar and passive radar net, target is detected, obtain not measuring value G in the same time of each target of passive radar _j(k):

Wherein, j represents the sequence number of target in passive radar, j=1, and 2 ..., N, N is the number that passive radar detects target, θ _j' (k),

be respectively j target at k constantly with respect to position angle and the angle of pitch of passive radar;

(3) each target of Active Radar obtaining according to step (1) is measuring value in the same time not, calculates each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value

and error covariance matrix R _i(k);

(4) each target of Active Radar obtaining according to step (3) is not in the same time with respect to the estimated value of the measuring value of passive radar

and each target of passive radar of obtaining of step (2) measuring value G in the same time not _j(k), calculate not correlation distance Γ in the same time of each target of Active Radar and each target of passive radar _ij(k);

(5) each target of Active Radar obtaining according to step (4) and each target of passive radar be correlation distance Γ in the same time not _ij(k), calculate each target of Active Radar and each target of passive radar discriminating statistic Ω in correlation time section _ij:

${\mathrm{\&Omega;}}_{\mathrm{ij}}=\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}{\mathrm{\&Gamma;}}_{\mathrm{ij}}\left(k\right),$

Wherein, m is the some finish time of section correlation time, 1≤m≤T;

(6) according to differentiating statistic Ω _ijthe card side that approximate obedience degree of freedom is 2m distributes, and calculates detection threshold δ:

$\mathrm{\&delta;}={\mathrm{\&chi;}}_{2m}^2\left(\mathrm{\&alpha;}\right),$

Wherein,

represent that the card side that degree of freedom is 2m distributes, the level of significance that α is test of hypothesis;

(7) discriminating statistic Ω step (5) being obtained _ijthe detection threshold δ obtaining with step (6) compares, and obtains Testing Association result U _ij:

If Ω _ij< δ, represents that i target in Active Radar and j target association in passive radar check successfully, U _ij=1;

If Ω _ij> δ, represents that i target in Active Radar and j target association in passive radar check unsuccessfully, U _ij=0;

(8) Testing Association result U step (7) being obtained _ijcarry out polysemy processing, obtain the Testing Association result after polysemy is processed

(9) the Testing Association result after the polysemy obtaining according to step (8) is processed

each target in Active Radar is carried out to true and false target discrimination:

If

think that i target in Active Radar is true target, retained;

If

think that i target in Active Radar is false targets, by its rejecting.

The present invention compared with prior art tool has the following advantages:

1,, than single portion radar, the present invention is due to the target observation information that can merge different sensors and obtain from different visual angles, therefore can more effectively identify deception false target jamming profile;

2,, than existing method, the present invention, owing to utilizing azimuth information and the angle of pitch information of passive radar in active radar and passive radar net simultaneously, therefore can reduce the mistake discrimination probability to decoy, more effectively identifies false targets and disturbs.

Accompanying drawing explanation

Fig. 1 is realization flow figure of the present invention;

Fig. 2 is active radar and passive radar net and the target information figure that emulation experiment of the present invention adopts;

Fig. 3 be with existing method and the inventive method to the discrimination probability of true target and to the mistake discrimination probability of decoy the change curve simulation result figure with segment length correlation time;

Fig. 4 be with existing method and the inventive method to the discrimination probability of true target and to the mistake discrimination probability of decoy the simulation result figure with the change curve of deception distance;

Fig. 5 be with existing method and the inventive method to the discrimination probability of true target and to the mistake discrimination probability of decoy the simulation result figure with the change curve of passive radar angle error.

Embodiment

With reference to Fig. 1, specific implementation step of the present invention is as follows:

Step 1: with the Active Radar in active radar and passive radar net, target is detected, obtain not measuring value Zi (k) in the same time of each target of Active Radar:

Wherein, i represents the sequence number of target in Active Radar, i=1, and 2 ..., M, M is the number that Active Radar detects target, k represents moment point, k=1,2 ..., T, T is active radar and passive radar net total moment point length detection time, r _i(k), θ _i(k),

be respectively i target at k constantly with respect to radial distance, position angle and the angle of pitch of Active Radar.

Step 2: with the passive radar in active radar and passive radar net, target is detected, obtain not measuring value G in the same time of each target of passive radar _j(k):

Wherein, j represents the sequence number of target in passive radar, j=1, and 2 ..., N, N is the number that passive radar detects target, θ _j' (k),

be respectively j target at k constantly with respect to position angle and the angle of pitch of passive radar.

Step 3: calculate each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value

Each target of Active Radar 3a) obtaining according to step (1) is measuring value Z in the same time not _i(k), calculate each target of Active Radar not in the same time at the coordinate of unifying under rectangular coordinate system: X _i(k)=[x _i(k), y _i(k), z _i(k)], wherein each target at the coordinate x of x axle _i(k), at the coordinate y of y axle _i(k) with at the coordinate z of z axle _i(k) be respectively:

In formula, x ₀, y ₀, z ₀be respectively x axial coordinate, y axial coordinate and the z axial coordinate of Active Radar under unified rectangular coordinate system;

3b) according to step 3a) each target of Active Radar of obtaining coordinate X under unified rectangular coordinate system in the same time not _i(k)=[x _i(k), y _i(k), z _i(k)], calculate each target in Active Radar not in the same time with respect to the estimated value of the measuring value of passive radar:

Wherein, azimuthal estimated value

estimated value with the angle of pitch

be respectively:

${\widehat{\mathrm{\&theta;}}}_i^{\&prime;}=\arctan \left(\frac{y_i\left(k\right)-{\mathrm{<figure-callout\; id="0"\; label="y"\; filenames="HDA0000457965560000041.png,HDA0000457965560000051.png"\; state="\{\{state\}\}">y</figure-callout>}}_0^{\&prime;}}{x_i\left(k\right)-x_0^{\&prime;}}\right),$

In formula, x ₀', y ₀', z ₀' be respectively passive radar at x axial coordinate, y axial coordinate and the z axial coordinate unified under rectangular coordinate system.

Step 4: calculate to calculate each target of Active Radar not in the same time with respect to passive radar estimated value

error covariance matrix R _i(k).

4a) according to each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value

in azimuthal estimated value

calculate partial derivative

with

Wherein, intermediate variable Δ x _i(k)=x _i(k)-x ₀', Δ y _i(k)=y _i(k)-y ₀';

4b) according to each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value

the estimated value of the middle angle of pitch

calculate partial derivative

with

Wherein, intermediate variable Δ z _i(k)=z _i(k)-z ₀';

4c) according to step 4a) partial derivative that obtains

with

and step 4b) partial derivative obtaining

with

tectonic transition matrix Q _i(k):

4d) according to step 4c) the transformation matrix Q that obtains _i(k), calculate each target of Active Radar not in the same time with respect to passive radar estimated value

error covariance matrix R _i(k):

R _i(k)＝Q _i(k)·Λ·(Q _i(k)) ^T，

Wherein, diag () represents to take the diagonal matrix that its variable is diagonal element, σ _rfor the range error of Active Radar, σ _θfor the azimuth angle error of Active Radar,

for the angle of pitch error of Active Radar, () ^tfor transposition computing.

Step 5: calculate not correlation distance Γ in the same time of each target of Active Radar and each target of passive radar _ij(k).

Each target of Active Radar 5a) obtaining according to step (3) is not in the same time with respect to the estimated value of passive radar measuring value and each target of passive radar of obtaining of step (2) measuring value G in the same time not _j(k), calculate not measuring value error delta G in the same time of each target of Active Radar and each target of passive radar _ij(k):

$\mathrm{\&Delta;}{G}_{\mathrm{ij}}\left(k\right)={\hat{G}}_i\left(k\right)-G_j\left(k\right),$

In Active Radar 5b) obtaining according to step (4), each target is not in the same time with respect to the estimated value of passive radar measuring value

error covariance matrix R _i(k), calculated amount monitoring error Δ G _ij(k) error covariance matrix P _ij(k):

P _ij(k)＝R _i(k)+Λ′，

Wherein, σ _θ' be the azimuth angle error of passive radar,

angle of pitch error for passive radar;

5c) according to measuring value error delta G _ij, and error covariance matrix P (k) _ij(k), calculate each target of Active Radar and each target of passive radar at correlation distance Γ in the same time not _ij(k):

${\mathrm{\&Gamma;}}_{\mathrm{ij}}\left(k\right)=\mathrm{\&Delta;}{G}_{\mathrm{ij}}\left(k\right)P_{\mathrm{ij}}^{-1}\left(k\right){\left(\mathrm{\&Delta;}{G}_{\mathrm{ij}}\left(k\right)\right)}^T,$

Wherein, () ^-1for inversion operation.

Step 6: each target of Active Radar obtaining according to step (5) and each target of passive radar be correlation distance Γ in the same time not _ij(k), calculate each target of Active Radar and each target of passive radar discriminating statistic Ω in correlation time section _ij:

${\mathrm{\&Omega;}}_{\mathrm{ij}}=\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}{\mathrm{\&Gamma;}}_{\mathrm{ij}}\left(k\right),$

Wherein, m is the some finish time of section correlation time, 1≤m≤T.

Step 7: the discriminating statistic Ω obtaining according to step (6) _ij, obtain Testing Association result U _ij.

7a) according to differentiating statistic Ω _ijthe card side that approximate obedience degree of freedom is 2m distributes, and calculates detection threshold δ:

$\mathrm{\&delta;}={\mathrm{\&chi;}}_{2m}^2\left(\mathrm{\&alpha;}\right),$

Wherein,

represent that the card side that degree of freedom is 2m distributes, the level of significance that α is test of hypothesis;

7b) will differentiate statistic Ω _ijδ compares with detection threshold, obtains Testing Association result U _ij:

If Ω _ij< δ, represents that i target in Active Radar and j target association in passive radar check successfully, U _ij=1;

If Ω _ij> δ, represents that i target in Active Radar and j target association in passive radar check unsuccessfully, U _ij=0.

Step 8: the Testing Association result U that step (7) is obtained _ijcarry out polysemy processing, obtain the Testing Association result after polysemy is processed

8a) judgement Testing Association result U _ijwhether there is polysemy:

If

be judged to Testing Association result U _ijthere is not polysemy,

If be judged to Testing Association result U _ijthere is polysemy, execution step 8b);

8b) to there is the Testing Association result U of polysemy _ij, find all targets of j target of passive radar and Active Radar to differentiate statistic Ω _1j, Ω _2j..., Ω _mjin minimum value, the target sequence number of remembering the Active Radar that this minimum value is corresponding is q;

8c) according to the target sequence number q of Active Radar, obtain the Testing Association result after polysemy is processed

${\tilde{U}}_{\mathrm{ij}}=\left\{\begin{array}{cc}1,& i=\mathrm{<figure-callout\; id="0"\; label="q"\; filenames="HDA0000457965560000041.png,HDA0000457965560000051.png"\; state="\{\{state\}\}">q</figure-callout>}\\ 0,& i\&NotEqual;q\end{array}\right..$

Step 9: the Testing Association result after the polysemy obtaining according to step (8) is processed

each target of Active Radar is carried out to true and false target discrimination:

If

think that i target in Active Radar is true target, retained;

If

think that i target in Active Radar is false targets, by its rejecting.

The present invention suppresses the ability of deception formula interference and can further verify by following emulation.

1. experiment scene:

As shown in Figure 2, active radar and passive radar net is comprised of an Active Radar and a passive radar, and the coordinate of Active Radar under unified rectangular coordinate system is (0,0,0) km, range error σ _r=25m, azimuth angle error σ _θ=0.3 °, angle of pitch error

the coordinate of passive radar under unified rectangular coordinate system is (20,0,0) km, azimuth angle error σ _θ'=1 °, angle of pitch error

in simulating scenes, there are a true target and two false targets, the initial coordinate of true target is (1.6,8,8) km, movement velocity is (10,-80,2) m/s, motion artifacts standard deviation is 0.5, these two false targets are distributed in the front and back of true target symmetrically, deception distance is 500m, and sampling time interval is 1s.

2. experiment content:

Experiment 1: the length that changes section correlation time, length to each of section correlation time, all existing method and the inventive method are carried out to Monte Carlo emulation experiment 1000 times, count respectively existing method and the inventive method to the discrimination probability of true target and to the mistake discrimination probability of decoy the change curve with segment length correlation time, result is as shown in Figure 3.Wherein, Fig. 3 (a) be existing method and the inventive method to the discrimination probability of true target the change curve with segment length correlation time, Fig. 3 (b) be existing method and the inventive method to the mistake discrimination probability of decoy the change curve with segment length correlation time.

Experiment 2: change deception distance, to each deception distance, all utilize existing method and the inventive method to carry out Monte Carlo emulation experiment 1000 times, count respectively existing method and the inventive method to the discrimination probability of true target and to the mistake discrimination probability of decoy with deception distance change curve, result is as shown in Figure 4.Wherein, Fig. 4 (a) be existing method and the inventive method to the discrimination probability of true target the change curve with deception distance, Fig. 4 (b) be existing method and the inventive method to the mistake discrimination probability of decoy with the change curve of cheating distance.

Experiment 3: the angle error that changes passive radar, to each angle error, all utilize existing method and the inventive method to carry out Monte Carlo emulation experiment 1000 times, count respectively existing method and the inventive method to the discrimination probability of true target and to the mistake discrimination probability of decoy the change curve with the angle error of passive radar, result is as shown in Figure 5.Wherein, Fig. 5 (a) be existing method and the inventive method to the discrimination probability of true target the change curve with the angle error of passive radar, Fig. 5 (b) be existing method and the inventive method to the mistake discrimination probability of decoy the change curve with the angle error of passive radar.

3. interpretation:

From Fig. 3 (a), can see, along with correlation time, the length of section is increasing, and existing method and the inventive method are all floated in 95% left and right to the discrimination probability of true target; From Fig. 3 (b), can see, along with correlation time, the length of section is increasing, existing method and the inventive method decline gradually to the discrimination probability of decoy, but the inventive method is all low than existing method to the mistake discrimination probability of decoy at each length correlation time place, explanation is under different correlation time length, the inventive method all can, under guaranteeing the condition of the discrimination probability of true target, effectively reduce the mistake discrimination probability of existing method to decoy.

From Fig. 4 (a), can see, along with deception distance is increasing, existing method and the inventive method are all floated in 95% left and right to the discrimination probability of true target; From Fig. 4 (b), can see, along with deception distance is increasing, existing method and the inventive method decline gradually to the mistake discrimination probability of decoy, but the inventive method is all low than existing method to the mistake discrimination probability of decoy at each length correlation time place, explanation is under different deception distances, the inventive method all can, under guaranteeing the condition of the discrimination probability of true target, effectively reduce the mistake discrimination probability of existing method to decoy.

From Fig. 5 (a), can see, along with the angle error of passive radar is increasing, existing method and the inventive method are all floated in 95% left and right to the discrimination probability of true target; From Fig. 5 (b), can see, along with the angle error of passive radar is increasing, existing method and the inventive method increase gradually to the mistake discrimination probability of decoy, but the inventive method is all low than existing method to the mistake discrimination probability of decoy each length correlation time place, explanation is under the different angle errors of passive radar, the inventive method all can, under guaranteeing the condition of the discrimination probability of true target, effectively reduce the mistake discrimination probability of existing method to decoy.

In sum, the present invention has reduced the mistake discrimination probability to decoy, has realized effective inhibition that false targets is disturbed.

Claims (5)

1. with the active radar and passive radar net of strange land configuration, suppress the method that false targets disturbs, comprise the steps:

(1) with the Active Radar in active radar and passive radar net, target is detected, obtain not measuring value Z in the same time of each target of Active Radar _i(k):

Wherein, i represents the sequence number of target in Active Radar, i=1, and 2 ..., M, M is the number that Active Radar detects target, k=1,2 ..., T, T is active radar and passive radar net total length detection time, r _i(k), θ _i(k),

be respectively i target at k constantly with respect to radial distance, position angle and the angle of pitch of Active Radar;

(2) with the passive radar in active radar and passive radar net, target is detected, obtain not measuring value G in the same time of each target of passive radar _j(k):

Wherein, j represents the sequence number of target in passive radar, j=1, and 2 ..., N, N is the number that passive radar detects target, θ _j' (k),

be respectively j target at k constantly with respect to position angle and the angle of pitch of passive radar;

(3) each target of Active Radar obtaining according to step (1) is measuring value in the same time not, calculates each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value

and error covariance matrix R _i(k);

(4) each target of Active Radar obtaining according to step (3) is not in the same time with respect to the estimated value of the measuring value of passive radar and each target of passive radar of obtaining of step (2) measuring value G in the same time not _j(k), calculate not correlation distance Γ in the same time of each target of Active Radar and each target of passive radar _ij(k);

(5) each target of Active Radar obtaining according to step (4) and each target of passive radar be correlation distance Γ in the same time not _ij(k), calculate each target of Active Radar and each target of passive radar discriminating statistic Ω in correlation time section _ij:

${\mathrm{\&Omega;}}_{\mathrm{ij}}=\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}{\mathrm{\&Gamma;}}_{\mathrm{ij}}\left(k\right),$

Wherein, m is the some finish time of section correlation time, 1≤m≤T;

(6) according to differentiating statistic Ω _ijthe card side that approximate obedience degree of freedom is 2m distributes, and calculates detection threshold δ:

$\mathrm{\&delta;}={\mathrm{\&chi;}}_{2m}^2\left(\mathrm{\&alpha;}\right),$

Wherein,

represent that the card side that degree of freedom is 2m distributes, the level of significance that α is test of hypothesis;

(7) discriminating statistic Ω step (5) being obtained _ijthe detection threshold δ obtaining with step (6) compares, and obtains Testing Association result U _ij:

If Ω _ij< δ, represents that i target in Active Radar and j target association in passive radar check successfully, U _ij=1;

If Ω _ij> δ, represents that i target in Active Radar and j target association in passive radar check unsuccessfully, U _ij=0;

(8) Testing Association result U step (7) being obtained _ijcarry out polysemy processing, obtain the Testing Association result after polysemy is processed

(9) the Testing Association result after the polysemy obtaining according to step (8) is processed

each target in Active Radar is carried out to true and false target discrimination:

If

think that i target in Active Radar is true target, retained;

If think that i target in Active Radar is false targets, by its rejecting.

2. the active radar and passive radar net with strange land configuration according to claim 1 suppresses the method that false targets disturbs, and calculates each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value in wherein said step (3)

carry out as follows:

3a) according to each target of Active Radar measuring value Z in the same time not _i(k), calculate each target of Active Radar not in the same time at the coordinate of unifying under rectangular coordinate system: X _i(k)=[x _i(k), y _i(k), z _i(k)], wherein each target at the coordinate x of x axle _i(k), at the coordinate y of y axle _i(k) with at the coordinate z of z axle _i(k) be respectively:

In formula, r _i(k), θ _i(k),

be respectively i target at k constantly with respect to radial distance, position angle and the angle of pitch of Active Radar, x ₀, y ₀, z ₀be respectively x axial coordinate, y axial coordinate and the z axial coordinate of Active Radar under unified rectangular coordinate system, i=1,2 ..., M, M is the number that Active Radar detects target, k=1,2 ..., T, T is active radar and passive radar net total length detection time;

3b) according to step 3a) each target of Active Radar of obtaining coordinate X under unified rectangular coordinate system in the same time not _i(k), calculate each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value:

Wherein, azimuthal estimated value estimated value with the angle of pitch

be respectively:

${\widehat{\mathrm{\&theta;}}}_i^{\&prime;}=\arctan \left(\frac{y_i\left(k\right)-y_0^{\&prime;}}{x_i\left(k\right)-x_0^{\&prime;}}\right),$

In formula, x ₀', y ₀', z ₀' be respectively passive radar at x axial coordinate, y axial coordinate and the z axial coordinate unified under rectangular coordinate system.

3. the active radar and passive radar net with strange land configuration according to claim 1 suppresses the method that false targets disturbs, and calculates error covariance matrix R in wherein said step (3) _i(k), carry out as follows:

3.1) according to each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value

in azimuthal estimated value

calculate partial derivative

with

Wherein, r _i(k), θ _i(k),

be respectively i target at k constantly with respect to radial distance, position angle and the angle of pitch of Active Radar, intermediate variable Δ x _i(k)=x _i(k)-x ₀', Δ y _i(k)=y _i(k)-y ₀', x _i(k), y _i(k) be respectively x axial coordinate, the y axial coordinate of i target under unified rectangular coordinate system, x ₀', y ₀' be respectively x axial coordinate, the y axial coordinate of passive radar under unified rectangular coordinate system, i=1,2 ..., M, M is the number that Active Radar detects target, k=1,2 ..., T, T is active radar and passive radar net total length detection time;

3.2) according to each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value

the estimated value of the middle angle of pitch

calculate partial derivative

with

Wherein, intermediate variable Δ z _i(k)=z _i(k)-z ₀', z _i(k) be the z axial coordinate of i target under unified rectangular coordinate system, z ₀' be the z axial coordinate of passive radar under unified rectangular coordinate system;

3.3) according to step 3.1) partial derivative that obtains

with

and step 3.2) partial derivative obtaining with

tectonic transition matrix Q _i(k):

3.4) according to step 3.3) the transformation matrix Q that obtains _i(k), error of calculation covariance matrix R _i(k):

R _i(k)＝Q _i(k)·Λ·(Q _i(k)) ^T，

Wherein,

diag () represents to take the diagonal matrix that its variable is diagonal element, σ _rfor the range error of Active Radar, σ _θfor the azimuth angle error of Active Radar,

for the angle of pitch error of Active Radar, () ^tfor transposition computing.

4. the active radar and passive radar net with strange land configuration according to claim 1 suppresses the method that false targets disturbs, wherein described each target of calculating Active Radar of step (4) and each target of passive radar correlation distance Γ in the same time not _ij(k), carry out as follows:

4a) according to each target of Active Radar not in the same time with respect to the estimated value of passive radar measuring value

with each target of passive radar measuring value G in the same time not _j(k), calculate not measuring value error delta G in the same time of each target of Active Radar and each target of passive radar _ij(k):

$\mathrm{\&Delta;}{G}_{\mathrm{ij}}\left(k\right)={\hat{G}}_i\left(k\right)-G_j\left(k\right),$

Wherein, i represents the sequence number of target in Active Radar, i=1,2 ..., M, M is the number that Active Radar detects target, and j represents the sequence number of target in passive radar, j=1,2 ..., N, N is the number that passive radar detects target, k=1,2, ..., T, T is active radar and passive radar net total length detection time;

4b) according to each target of Active Radar not in the same time with respect to the estimated value of the measuring value of passive radar

error covariance matrix R _i(k), calculated amount monitoring error Δ G _ij(k) error covariance matrix P _ij(k):

P _ij(k)＝R _i(k)+Λ′，

Wherein, diag () represents to take the diagonal matrix that its variable is diagonal element, σ _θ' be the azimuth angle error of passive radar,

angle of pitch error for passive radar;

4c) according to measuring value error delta G _ij, and error covariance matrix P (k) _ij(k), calculate not correlation distance Γ in the same time of each target of Active Radar and each target of passive radar _ij(k):

${\mathrm{\&Gamma;}}_{\mathrm{ij}}\left(k\right)=\mathrm{\&Delta;}{G}_{\mathrm{ij}}\left(k\right)P_{\mathrm{ij}}^{-1}\left(k\right){\left(\mathrm{\&Delta;}{G}_{\mathrm{ij}}\left(k\right)\right)}^T,$

Wherein, () ^-1for inversion operation, () ^tfor transposition computing.

5. the active radar and passive radar net with strange land configuration according to claim 1 suppresses the method that false targets disturbs, wherein step (8) described to Testing Association result U _ijcarry out polysemy processing, obtain the Testing Association result after polysemy is processed carry out as follows:

8a) judgement Testing Association result U _ijwhether there is polysemy:

If

be judged to Testing Association result U _ijthere is not polysemy,

If be judged to Testing Association result U _ijthere is polysemy, execution step 8b);

Wherein, i represents the sequence number of target in Active Radar, i=1, and 2 ..., M, M is the number that Active Radar detects target, j represents the sequence number of target in passive radar, j=1,2 ..., N, N is the number that passive radar detects target;

8b) to there is the Testing Association result U of polysemy _ij, find all targets of j target of passive radar and Active Radar to differentiate statistic Ω _1j, Ω _2j..., Ω _mjin minimum value, the target sequence number of remembering the Active Radar that this minimum value is corresponding is q;

8c) according to the target sequence number q of Active Radar, obtain the Testing Association result after polysemy is processed

${\tilde{U}}_{\mathrm{ij}}=\left\{\begin{array}{cc}1,& i=q\\ 0,& i\&NotEqual;q\end{array}\right..$

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