CN104991233A - Networking radar anti-cheating interference method based on signal level fusion - Google Patents

Abstract

The invention provides a networking radar anti-cheating interference method based on signal level fusion, and the method can achieve the effective discrimination of false targets generated by different cheating interferences. The method comprises the steps: 1, building slow-time random complex envelope sequences of each point target at a plurality of junction radars; 2, estimating the mean power of each point target at each junction radar; 3, enabling the slow-time random complex envelope sequences of the same point target at the plurality of junction radars to be combined in a paired manner to form a plurality of envelope groups, and estimating a correlation coefficient; 4, selecting the real part of the correlation coefficient of each envelope group as the correlation tolerance; 5, calculating the detection threshold of the correlation tolerance corresponding to each envelope group; 6, judging whether the correlation tolerance is greater than the detection threshold or not: judging that the envelope groups pass through false target examination if the correlation tolerance is not greater than the detection threshold, or else, marking two corresponding point targets as false targets; 7, eliminating the false targets.

Description

Based on the anti-Deceiving interference method of radar network that signal level merges

Technical field

The present invention relates to Radar Technology field, particularly a kind of anti-Deceiving interference method of radar network merged based on signal level.

Background technology

The message contexts such as spoofing techniques is devoted in direction, position, tracking starting point are cheated radar of being injured or around real goal echo, are manufactured a lot of decoy to such an extent as to actual target information can not be extracted.A kind of effective spoofing techniques classification is deception formula spoofing techniques.The object of deception is by the transmitting of modulation or forwards and receive the information such as amplitude, phase place of echo to radar and mislead.Especially the appearance of digital radiofrequency memory (Digital Radio Frequency Memory, DRFM) makes Cheat Jamming Technique more ripe, and the translation jammer applying DRFM is widely used in self-defence type interference and goes along with the team in interference.Deceiving interference can take a large amount of system resource, has a strong impact on detection and the tracking performance of radar system.

For false targets interference, monostatic radar is single due to visual angle, be difficult to resist it, and the method for radar network utilisation point mark association carries out true and false differentiation to the target detected, and weeds out decoy, thus realize the antagonism of Deceiving interference.But, because node radar each in radar network all can be subject to Deceiving interference, intensive decoy can cause the error rate of carrying out Testing Association between the measuring value of each node radar higher, and radar network cloth station location is undesirable, also can affect the ability of radar network antagonism Deceiving interference.

Existing radar network is all utilize pixel-based fusion to resist Deceiving interference, in the process of radar to target measurement, only make use of some mark information or the flight path information of target, but other information is not effective to be utilized, therefore, pixel-based fusion anti-interference method can not play its antijamming capability completely, cannot make full use of radar network composite advantage.

Summary of the invention

For the deficiency of above-mentioned existing method antagonism false targets interference, the object of the invention is to propose a kind of anti-Deceiving interference method of networking radar system signal level, can the decoy that different Deceiving interference produces effectively be differentiated.

In order to achieve the above object, the present invention is achieved by the following technical solutions.

Based on the anti-Deceiving interference method of radar network that signal level merges, described radar network comprises multiple node radar, said method comprising the steps of:

Step 1, the complex magnitude of the echo data of point target after calculating matched filtering, and construct the slow time random complex envelope sequence of each point target at described multiple node radar according to described complex magnitude;

Step 2, according to the slow time random complex envelope sequence of described each point target at described multiple node radar, estimates the average power of each point target at each node radar;

Step 3, for same point target, by its random complex envelope sequence combination of two of slow time at described multiple node radar, forms multiple envelope group, and estimates the related coefficient of each envelope group;

Step 4, for same point target, the real part choosing the related coefficient of described each envelope group is respectively as relativity measurement corresponding to each envelope group;

Step 5, the real goal probability of miscarriage of justice of given radar network, and the inspection thresholding calculating relativity measurement corresponding to described each envelope group according to the real goal probability of miscarriage of justice of described radar network;

Step 6, described relativity measurement and described inspection thresholding are compared, judge whether described relativity measurement is greater than described inspection thresholding, when described relativity measurement is less than or equal to described inspection thresholding, judge that the envelope group that described relativity measurement is corresponding is checked by decoy; When described relativity measurement is greater than described inspection thresholding, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy;

Step 7, rejects described decoy.

Preferably, described step 1 comprises following sub-step:

1a) establish described radar network to comprise n node radar, wherein n >=2, each node radar receives echo data, and adopts following formula to carry out matched filtering to described echo data, obtains echo data y (t) of point target after matched filtering:

$y\left(t\right)=x\left(t\right)\⊗x^{*}(-t)$

Wherein, x (t) is echo data, for convolution symbol, * represents conjugation;

1b) adopt following formula to carry out coherent accumulation to the echo data of point target after described matched filtering, obtain echo data Y (k) of point target after coherent accumulation:

$Y\left(k\right)=\underset{m=0}{\overset{Q-1}{\Σ}}y\left(m\right)e^{-j\frac{2\mathrm{\π}}{Q}km}$

Wherein, Q is pulse accumulation number, and y (m) is the echo data of point target after matched filtering;

After 1c) establishing described coherent accumulation, the echo data of point target comprises the echo data of P point target, carries out CFAR detection, obtain the complex magnitude of the echo data of P point target respectively to the echo data of point target after described coherent accumulation;

1d) by the set of the complex magnitude of the echo data of described P point target composition, and using the slow time random complex envelope sequence of described set as P point target

$X_{p_i}^i=\{A_{p_i}^i,i=1,2,3......,n\},p_i=1,2,3....P$

Wherein, n represents the node radar number in described radar network, and n>=2; represent the p that i-th node detections of radar in described n node radar arrives _ithe complex magnitude of the echo data of individual point target; be a matrix, line number is all umber of pulses in each Coherent processing cycle, and columns is the number P of point target.

Preferably, described step 2 comprises following sub-step:

Described radar network 2a) is established to comprise n node radar, wherein n>=2, if the echo data of point target comprises P point target after described matched filtering, from described n node radar, choose i-th node radar, and choose the p that described i-th node detections of radar arrive _ithe complex magnitude of the echo data of individual point target

2b) according to the p that described i-th node detections of radar arrives _ithe complex magnitude of the echo data of individual point target by point target p described in following formulae discovery _iin the estimated value of the average power of i-th node radar

${\mathrm{\ζ}}_{p_i,i}^2=\frac{{A_{p_i}^i}^H{A}_{p_i}^i}{Q},i=1,2,3,...,n$

Wherein, Q is the conjugate transpose of the number of PRT in the Coherent processing cycle, H representing matrix.

Preferably, described step 3 comprises following sub-step:

Described radar network 3a) is established to comprise n node radar, wherein n>=2, if the echo data of point target comprises P point target after described matched filtering; From described n node radar, choose i-th node radar and a jth node radar, from a described P point target, choose p point target, for described i-th node radar, described p point target is p _iindividual point target; For a described jth node radar, described p point target is p _jindividual point target, by the p of described i-th node detections of radar _ithe random complex envelope sequence of slow time of individual point target with the p of a jth node detections of radar _jthe random complex envelope sequence of slow time of individual point target combine, form envelope group;

3b) by described in following formulae discovery with the related coefficient of the envelope group formed

${\widehat{\mathrm{\ρ}}}_{p_i,p_j}={\left(X_{p_i}^i\right)}^H{X}_{p_j}^j,i\≠j,i=1,2,3,...n,j=1,2,3...n$

Wherein, the conjugate transpose of H representing matrix;

3c) repeat 3b) to the related coefficient obtaining each envelope group.

Preferably, described step 4 comprises following sub-step:

According to the related coefficient of each envelope group its real part is chosen as relativity measurement corresponding to each envelope group by following formula

${\mathrm{\μ}}_{p_i,p_j}=real\left({\widehat{\mathrm{\ρ}}}_{p_i,p_j}\right)$

Wherein, real () represents right get real part.

Preferably, described step 5 comprises following sub-step:

5a) the real goal probability of miscarriage of justice P of given radar network _l;

5b) according to the real goal probability of miscarriage of justice P of described radar network _l, by the inspection thresholding of calculation of correlation corresponding to following formulae discovery each envelope group

${\mathrm{\ξ}}_{p_i,p_j}=\sqrt{{\mathrm{Q\ζ}}_{p_i,i}^2{\mathrm{\ζ}}_{p_j,j}^2/2}\·{\mathrm{\Φ}}^{-1}(1-{(1-P_l)}^{1/P})$

Wherein, Φ () represents standardized normal distribution, and Q is the number of PRT in the Coherent processing cycle, and P detects the target number obtained, represent point target p _iin the average power of i-th node radar and a jth node radar.

Preferably, described step 6 comprises following sub-step:

6a) by described relativity measurement with described inspection thresholding compare;

6b) when time, judge that the envelope group that described relativity measurement is corresponding is checked by decoy;

6c) when time, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy.

Preferably, described step 7 comprises following sub-step:

7a) search the relativity measurement that the envelope group at two the point target places being demarcated as decoy is corresponding;

7b) complex magnitude of point target echo data corresponding for described relativity measurement is set to zero.

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

First, the present invention utilizes the complex envelope of real goal echo separate and the relevant feature of undesired signal complex envelope, by step 3 for same point target, by its random complex envelope sequence combination of two of slow time at described multiple node radar, form multiple envelope group, and estimate the related coefficient of each envelope group, because the echo related coefficient of real goal is smaller, therefore signal level fusion treatment can be utilized, the information of target is made to obtain higher utilization rate, finally can more effective antagonism Deceiving interference.

The second, the present invention only uses signal envelope, does not occur any modulation system, therefore, it is possible to do not rely on the signal madulation mode of Deceiving interference, thus effectively can differentiate the decoy that different Deceiving interference mode produces.

3rd, the present invention can be used for networking radar system fusion center, by carrying out correlation test to the envelope of target, can differentiate the active decoy that Deceiving interference produces, realizing networking radar system and effectively resisting Deceiving interference.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of a kind of anti-Deceiving interference method of radar network based on signal level fusion of the present invention;

Fig. 2 is the realization flow figure of the anti-Deceiving interference method of networking radar system of the present invention;

When Fig. 3 is TNR=0dB, 3dB, 6dB, 9dB, the correct discrimination probability P of decoy _fTwith the change curve of accumulation pulse number Q, wherein, horizontal ordinate is pulse accumulation number Q, and ordinate is discrimination probability P _fT;

Fig. 4 be M=2,4,8,14 time, the correct discrimination probability P of decoy _fTwith the change curve of accumulation pulse number Q, wherein, horizontal ordinate is pulse accumulation number Q, and ordinate is discrimination probability P _fT;

Fig. 5 is P _l=0.01,0.005, when 0.001, the correct discrimination probability P of decoy _fTwith the change curve of accumulation pulse number Q, wherein, horizontal ordinate is pulse accumulation number Q, and ordinate is discrimination probability P _fT.

Embodiment

For enabling above-mentioned purpose of the present invention, feature and advantage become apparent more, and below in conjunction with the drawings and specific embodiments, the present invention is further detailed explanation.

Embodiment one:

With reference to Fig. 1, show the process flow diagram of a kind of anti-Deceiving interference method of radar network based on signal level fusion of the embodiment of the present invention, the present embodiment specifically can comprise the following steps:

Step 1, the complex magnitude of the echo data of point target after calculating matched filtering, and construct the slow time random complex envelope sequence of each point target at described multiple node radar according to described complex magnitude.

Described radar network is established to comprise n node radar, wherein n >=2 in the present embodiment, if the echo data of point target comprises P point target after described matched filtering.

Step 1 described in the present embodiment comprises following sub-step:

1a) establish described radar network to comprise n node radar, wherein n >=2, each node radar receives echo data, and adopts following formula to carry out matched filtering to described echo data, obtains echo data y (t) of point target after matched filtering:

$y\left(t\right)=x\left(t\right)\⊗x^{*}(-t)$

Wherein, x (t) is echo data, for convolution symbol, * represents conjugation.

It should be noted that, n node radar is included in the radar network that the present embodiment adopts, n >=2, each node radar receives echoed signal, and carries out matched filtering 1a to the echoed signal received), coherent accumulation 1b) and CFAR detection 1c).The present embodiment adopts above-mentioned formula to carry out matched filtering to described echo data, namely adopt echo data and its conjugation reverse after the method for convolution of functions carry out matched filtering.Need to further illustrate, echo data comprises real goal, decoy, noise (true and false target general designation signal), and matched filtering can improve signal to noise ratio (S/N ratio), therefore more clearly can highlight the echo data of point target.

1b) adopt following formula to carry out coherent accumulation to the echo data of point target after described matched filtering, obtain echo data Y (k) of point target after coherent accumulation:

$Y\left(k\right)=\underset{m=0}{\overset{Q-1}{\Σ}}y\left(m\right)e^{-j\frac{2\mathrm{\π}}{Q}km}$

Wherein, Q is pulse accumulation number, and y (m) is the echo data of point target after matched filtering.

It should be noted that, coherent accumulation refers to and the data point reuse phase place of different for same node radar pulse repetition time is added again, object improves signal to noise ratio (S/N ratio), after coherent accumulation, the echo data signal to noise ratio (S/N ratio) of point target is larger, the present embodiment adopts above-mentioned formula to carry out coherent accumulation to the echo data of point target after described matched filtering, namely adopt the mode to the data of same node radar different pulse repetition time carry out discrete Fourier transform (DFT) to carry out coherent accumulation, discrete Fourier transform (DFT) formula is above-mentioned formula.

After 1c) establishing described coherent accumulation, the echo data of point target comprises the echo data of P point target, carries out CFAR detection, obtain the complex magnitude of the echo data of P point target respectively to the echo data of point target after described coherent accumulation.

It should be noted that, CFAR detection refers to and echo data and given thresholding is compared, and object is that true and false target echo data are extracted from noise background, can obtain the complex magnitude of point target echo data after CFAR detection the present embodiment above-mentioned steps 1a), 1b), 1c) " calculating the complex magnitudes of matched filtering back echo data " in corresponding step 1.

1d) by the set of the complex magnitude of the echo data of described P point target composition, and using the slow time random complex envelope sequence of described set as P point target

$X_{p_i}^i=\{A_{p_i}^i,i=1,2,3...\mathrm{..},n\},p_i=1,2,3....P$

Wherein, n represents the node radar number in described radar network, and n>=2; represent the p that i-th node detections of radar in described n node radar arrives _ithe complex magnitude of the echo data of individual point target; be a matrix, line number is all umber of pulses in each Coherent processing cycle, and columns is the number P of point target.

It should be noted that, above-mentioned for matrix, the complex magnitude composition set of the echo data of a described P point target in each element be matrix.It should be noted that, above-mentioned steps 1d) the random complex envelope sequence of slow time constructing each point target according to described complex magnitude in corresponding step 1, namely the present embodiment is using the slow time random complex envelope sequence of the set of the complex magnitude of described matched filtering back echo data as each point target.The present embodiment repeats above-mentioned 1a all pulse-recurrence times (Pulse Recurrence Time, PRT) in a Coherent processing cycle), 1b), 1c).

Step 2, according to the slow time random complex envelope sequence of described each point target at described multiple node radar, estimates the average power of each point target at each node radar.

Step 2 described in the present embodiment comprises following sub-step:

Described radar network 2a) is established to comprise n node radar, wherein n>=2, if the echo data of point target comprises P point target after described matched filtering, from described n node radar, choose i-th node radar, and choose the p that described i-th node detections of radar arrive _ithe complex magnitude of the echo data of individual point target

2b) according to the p that described i-th node detections of radar arrives _ithe complex magnitude of the echo data of individual point target by point target p described in following formulae discovery _iin the estimated value of the average power of i-th node radar

${\mathrm{\ζ}}_{p_i,i}^2=\frac{{A_{p_i}^i}^H{A}_{p_i}^i}{Q},i=1,2,3,...,n$

Wherein, Q is the conjugate transpose of the number of PRT in the Coherent processing cycle, H representing matrix.

It should be noted that, the present embodiment 2a), 2b) be with the p arrived according to the i-th node detections of radar _ithe random complex envelope sequence of slow time of individual point target calculate described point target p _iin the estimated value of the average power of node i for example is described, the calculating of each point target average power is similar with it, the present embodiment repeats no more.And step 2 estimates each point target of the obtaining step 5b of average power below at each node) in use.

Step 3, for same point target, by its random complex envelope sequence combination of two of slow time at described multiple node radar, forms multiple envelope group, and estimates the related coefficient of each envelope group.

Step 3 described in the present embodiment comprises following sub-step:

Described radar network 3a) is established to comprise n node radar, wherein n>=2, if the echo data of point target comprises P point target after described matched filtering; From described n node radar, choose i-th node radar and a jth node radar, from a described P point target, choose p point target, for described i-th node radar, described p point target is p _iindividual point target; For a described jth node radar, described p point target is p _jindividual point target, by the p of described i-th node detections of radar _ithe random complex envelope sequence of slow time of individual point target with the p of a jth node detections of radar _jthe random complex envelope sequence of slow time of individual point target combine, form envelope group;

3b) by described in following formulae discovery with the related coefficient of the envelope group formed

${\widehat{\mathrm{\ρ}}}_{p_i,p_j}={\left(X_{p_i}^i\right)}^H{X}_{p_j}^j,i\≠j,i=1,2,3,...n,j=1,2,3...n$

Wherein, the conjugate transpose of H representing matrix.

3c) repeat 3b) to the related coefficient obtaining each envelope group.

The present embodiment can travel through all nodes, makes all point targets of all nodes repeat 3b), finally obtain the related coefficient of each envelope group.

Step 4, for same point target, the real part choosing the related coefficient of described each envelope group is respectively as relativity measurement corresponding to each envelope group.

It should be noted that, the related coefficient of described each envelope group is plural number, chooses the real part of related coefficient as relativity measurement in the present embodiment.

According to the related coefficient of each envelope group in the present embodiment its real part is chosen as relativity measurement corresponding to each envelope group by following formula

${\mathrm{\μ}}_{p_i,p_j}=real\left({\widehat{\mathrm{\ρ}}}_{p_i,p_j}\right)$

Wherein, real () represents right get real part.

Step 5, the real goal probability of miscarriage of justice of given radar network, and the inspection thresholding calculating relativity measurement corresponding to described each envelope group according to the real goal probability of miscarriage of justice of described radar network.

Step 5 described in the present embodiment comprises following sub-step:

5a) the real goal probability of miscarriage of justice P of given radar network _l;

5b) according to the real goal probability of miscarriage of justice P of described radar network _l, by the inspection thresholding of calculation of correlation corresponding to following formulae discovery each envelope group

${\mathrm{\ξ}}_{p_i,p_j}=\sqrt{{\mathrm{Q\ζ}}_{p_i,i}^2{\mathrm{\ζ}}_{p_j,j}^2/2}\·{\mathrm{\Φ}}^{-1}(1-{(1-P_l)}^{1/P})$

Wherein, Φ () represents standardized normal distribution, and Q is the number of PRT in the Coherent processing cycle, and P detects the target number obtained, represent point target p _iin the average power of i-th node radar and a jth node radar.

Step 6, described relativity measurement and described inspection thresholding are compared, judge whether described relativity measurement is greater than described inspection thresholding, when described relativity measurement is less than or equal to described inspection thresholding, judge that the envelope group that described relativity measurement is corresponding is checked by decoy; When described relativity measurement is greater than described inspection thresholding, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy.

It should be noted that, described relativity measurement and described inspection thresholding compare by the present embodiment, judge whether described relativity measurement is greater than the process of described inspection thresholding, namely carries out test of hypothesis to described relativity measurement.

Step 6 described in the present embodiment comprises following sub-step:

6a) by described relativity measurement with described inspection thresholding compare;

6b) when time, judge that the envelope group that described relativity measurement is corresponding is checked by decoy;

It should be noted that, if envelope group is checked by decoy, then two point targets that this envelope group is corresponding are checked by decoy;

6c) when time, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy.

Step 7, rejects described decoy.

Step 7 described in the present embodiment comprises following sub-step:

7a) search the relativity measurement that the envelope group at two the point target places being demarcated as decoy is corresponding;

7b) complex magnitude of point target echo data corresponding for described relativity measurement is set to zero.

It should be noted that, according to the description in above-mentioned steps 3, the random complex envelope Sequence composition envelope group of slow time of the point target of any two nodes, and according to the description in step 4, relativity measurement is for envelope group, therefore above-mentioned steps 7a) first find out at 6c) in be demarcated as relativity measurement corresponding to the envelope group at two point target places of decoy.According to 1d) in description, the random complex envelope sequence of slow time of each point target it is the complex magnitude of point target echo data set, and envelope group chooses the random complex envelope Sequence composition of slow time of the point target of any two nodes, envelope group again with described relativity measurement, therefore can find the complex magnitude of corresponding point target echo data according to described relativity measurement, then the complex magnitude of point target echo data corresponding for described relativity measurement is set to zero.

It should be noted that, the complex magnitude of point target echo data corresponding for described relativity measurement is set to zero, random complex envelope sequence of slow time can be made in only have the complex envelope sequence of real goal, thus reject described decoy.After the present embodiment finds out decoy in step 6, directly delete described decoy in step 7, thus realize the object of anti-Deceiving interference.

It should be noted that, the present embodiment is handled as follows each envelope group:

Each point target traveled through in inspection i-th node and jth node radar combines reject its decoy.To the p that each point target in the i-th node radar is carried out _jsecondary inspection, is once listed in active decoy, then reject this active decoy, to the p that each point target in jth node radar is carried out _isecondary inspection, is once listed in active decoy, then reject active decoy.Namely successively the inspection of step 2 to step 6 is carried out to the target complete in any two node radars, reject active decoy, thus realize the object of anti-Deceiving interference.

Embodiment two:

With reference to Fig. 2, show the process flow diagram of a kind of anti-Deceiving interference method of radar network based on signal level fusion of the embodiment of the present invention,

Described radar network is established to comprise n node radar, wherein n >=2 in the present embodiment, if the echo data of point target comprises P point target after described matched filtering.

The present embodiment specifically can comprise the following steps:

Step 201, gets the random complex envelope sequence of slow time of each target of i-th node.

It should be noted that, described step 201 is corresponding with the step 1 in embodiment one, can see the associated description of step 1 in embodiment one, and the present embodiment does not repeat at this.

Step 202, gets the random complex envelope sequence of slow time of each target of a jth node.

It should be noted that, described step 202 is corresponding with the step 1 in embodiment one, can see the associated description of step 1 in embodiment one, and the present embodiment does not repeat at this.

Step 203, estimates average power;

It should be noted that, in the present embodiment, step 203 is according to random complex envelope sequence of the slow time of each target of get in step 201 i-th node, estimates the average power of its correspondence.Step 203 is corresponding with the step 2 in embodiment one, and estimate the associated description of particular content see step 2 in embodiment one of average power, the present embodiment does not repeat at this.

Step 204, estimates average power;

It should be noted that, in the present embodiment, step 204 is according to random complex envelope sequence of the slow time of each target of the jth of getting in step 202 node, estimates the average power of its correspondence.Step 204 is corresponding with the step 2 in embodiment one, and estimate the associated description of particular content see step 2 in embodiment one of average power, the present embodiment does not repeat at this.

Step 205, estimates related coefficient.

It should be noted that, in the present embodiment, step 205 is random complex envelope sequences of slow time of each target of i-th node step 201 got, the random complex envelope Sequence composition envelope group of slow time of each target of the jth node got with step 202, then estimates the related coefficient of this envelope group.Step 205 is corresponding with the step 3 in embodiment one, and estimate the associated description of particular content see step 3 in embodiment one of related coefficient, the present embodiment does not repeat at this.

Step 206, gets real part and obtains relativity measurement.

It should be noted that, step 206 is to related coefficient get real part and obtain relativity measurement step 206 is corresponding with the step 4 in embodiment one, and get the associated description of particular content see step 4 in embodiment one of real part, the present embodiment does not repeat at this.

Step 207, calculates inspection thresholding.

It should be noted that, step 207 calculates inspection thresholding step 207 is corresponding with the step 5 in embodiment one, and calculate the associated description of particular content see step 5 in embodiment one of inspection thresholding, the present embodiment does not repeat at this.

Step 208, judges whether relativity measurement is greater than inspection thresholding.

It should be noted that, step 208 is by relativity measurement with inspection thresholding compare, judge relativity measurement whether be greater than inspection thresholding when time, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets be demarcated as active decoy, namely when time, perform step 209.Step 208 is corresponding with the step 6 in embodiment one, and particular content is see the associated description of step 6 in embodiment one, and the present embodiment does not repeat at this.

Step 209, draws active decoy position.

It should be noted that, when time, judge that the envelope group that relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy, namely drawn the position of active decoy.Step 209 and the step 6c in embodiment one) corresponding, particular content is see step 6c in embodiment one) associated description, the present embodiment does not repeat at this.

Step 210, rejects described decoy.

It should be noted that, step 210 is corresponding with the step 7 in embodiment one, and particular content is see the associated description of step 7 in embodiment one, and the present embodiment does not repeat at this.

The ability that the present invention resists Deceiving interference is verified further by following emulation.

(1) experiment scene

Carry out emulation experiment for the radar network of two node radar compositions, first node radar is operated in emission state, and second node radar is operated in accepting state, detects the same space region.A real goal is only had in common search coverage.

After supposing that the Received signal strength of two node radars carries out pulse compression, the target noise obtaining the complex envelope sequence of real goal is more equal than TNR, and the target noise of the complex envelope sequence of active decoy is more equal than TNR.

(2) experiment content and interpretation of result

Experiment one: the discriminating performance of active decoy discrimination method that the present invention proposes with target noise than TNR, decoy number M, real goal probability of miscarriage of justice P _land different accumulation pulse number Q is relevant.If P _l=0.001, M=2, TNR are taken as 0dB, 3dB, 6dB, 9dB respectively, and the value to each TNR, all under difference accumulation pulse number Q, carry out 100,000 Moto Carlo Monte Carlo experiment to the active decoy discrimination method that the present invention proposes, statistics obtains the correct discrimination probability P of decoy _fT, as shown in Figure 3.Wherein, the variation range accumulating pulse number Q is 4 ~ 64.

As can see from Figure 3, along with accumulation pulse number constantly increases, P _fTcontinuous increase, this is that relativity measurement is more effective to the estimated result of related coefficient, better to the discriminating performance of decoy because accumulation umber of pulse is more; TNR is larger, P _fThigher, this is because TNR is larger, with theoretical related coefficient under the decoy condition that corresponding same Deceiving interference signal produces is larger, and the difference of test statistics increases, then differentiate that performance is better.Accumulation pulse number Q differentiates that between 40 ~ 60 effect is better.

Experiment two: establish TNR=0dB, P _l=0.001, M is taken as 2,4,8,14 respectively, and the value to each M, all under difference accumulation pulse number Q, carry out 100,000 Moto Carlo Monte Carlo experiment to the active decoy discrimination method that the present invention proposes, statistics obtains the correct discrimination probability P of decoy _fT, as shown in Figure 4.Wherein, the variation range accumulating pulse number Q is 4 ~ 64.

As can see from Figure 4, along with accumulation pulse number constantly increases, P _fTcontinuous increase, this is that relativity measurement is more effective to the estimated result of related coefficient, better to the discriminating performance of decoy because accumulation umber of pulse is more; Along with active decoy number is on the increase, P _fTslow decline, at accumulation pulse number Q for differentiate that between 40 ~ 60 effect is better.

Experiment three: establish TNR=0dB, M=2, P _l=0.01,0.005,0.001, and to each P _lvalue, all under difference accumulation pulse number Q, carry out 100,000 Moto Carlo Monte Carlo experiment to the active decoy discrimination method that the present invention proposes, statistics obtains the correct discrimination probability P of decoy _fT, as shown in Figure 5.Wherein, the variation range accumulating pulse number Q is 4 ~ 64.

As can see from Figure 5, along with accumulation pulse number constantly increases, P _fTcontinuous increase, this is that relativity measurement is more effective to the estimated result of related coefficient, better to the discriminating performance of decoy because accumulation umber of pulse is more; Along with real goal probability of miscarriage of justice P _lincrease, P _fTslow rising, at accumulation pulse number Q for differentiate that between 40 ~ 60 effect is better.

For aforesaid each embodiment of the method, in order to simple description, therefore it is all expressed as a series of combination of actions, but those skilled in the art should know, the present invention is not by the restriction of described sequence of movement, because according to the present invention, some step can adopt other orders or carry out simultaneously.Secondly, those skilled in the art also should know, the embodiment described in instructions all belongs to preferred embodiment, and involved action and module might not be that the present invention is necessary.

Each embodiment in this instructions all adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar part mutually see.

The present invention can describe in the general context of computer executable instructions, such as program module.Usually, program module comprises the routine, program, object, assembly, data structure etc. that perform particular task or realize particular abstract data type.Also can put into practice the present invention in a distributed computing environment, in these distributed computing environment, be executed the task by the remote processing devices be connected by communication network.In a distributed computing environment, program module can be arranged in the local and remote computer-readable storage medium comprising memory device.

Finally, also it should be noted that, in this article, the such as relational terms of first and second grades and so on is only used for an entity or operation to separate with another entity or operational zone, and not necessarily requires or imply the relation that there is any this reality between these entities or operation or sequentially.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, commodity or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, commodity or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, commodity or the equipment comprising described key element and also there is other identical element.

Above to a kind of anti-Deceiving interference method of radar network merged based on signal level provided by the present invention, be described in detail, apply specific case herein to set forth principle of the present invention and embodiment, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping; Meanwhile, for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.

Claims (8)

1., based on the anti-Deceiving interference method of radar network that signal level merges, described radar network comprises multiple node radar, it is characterized in that, said method comprising the steps of:

Step 1, the complex magnitude of the echo data of point target after calculating matched filtering, and construct the slow time random complex envelope sequence of each point target at described multiple node radar according to described complex magnitude;

Step 2, according to the slow time random complex envelope sequence of described each point target at described multiple node radar, estimates the average power of each point target at each node radar;

Step 3, for same point target, by its random complex envelope sequence combination of two of slow time at described multiple node radar, forms multiple envelope group, and estimates the related coefficient of each envelope group;

Step 4, for same point target, the real part choosing the related coefficient of described each envelope group is respectively as relativity measurement corresponding to each envelope group;

Step 5, the real goal probability of miscarriage of justice of given radar network, and the inspection thresholding calculating relativity measurement corresponding to described each envelope group according to the real goal probability of miscarriage of justice of described radar network;

Step 6, described relativity measurement and described inspection thresholding are compared, judge whether described relativity measurement is greater than described inspection thresholding, when described relativity measurement is less than or equal to described inspection thresholding, judge that the envelope group that described relativity measurement is corresponding is checked by decoy; When described relativity measurement is greater than described inspection thresholding, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy;

Step 7, rejects described decoy.

2. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 1 comprises following sub-step:

1a) establish described radar network to comprise n node radar, wherein n >=2, each node radar receives echo data, and adopts following formula to carry out matched filtering to described echo data, obtains echo data y (t) of point target after matched filtering:

$y\left(t\right)=x\left(t\right)\⊗x*(-t)$

Wherein, x (t) is echo data, for convolution symbol, * represents conjugation;

1b) adopt following formula to carry out coherent accumulation to the echo data of point target after described matched filtering, obtain echo data Y (k) of point target after coherent accumulation:

$Y\left(k\right)=\underset{m=0}{\overset{Q-1}{\Σ}}y\left(m\right)e^{-j\frac{2\mathrm{\π}}{Q}km}$

Wherein, Q is pulse accumulation number, and y (m) is the echo data of point target after matched filtering;

After 1c) establishing described coherent accumulation, the echo data of point target comprises the echo data of P point target, carries out CFAR detection, obtain the complex magnitude of the echo data of P point target respectively to the echo data of point target after described coherent accumulation;

1d) by the set of the complex magnitude of the echo data of described P point target composition, and using the slow time random complex envelope sequence of described set as P point target

$X_{p_i}^i=\{A_{pi}^i,i=1,2,\mathrm{3.....},n\},p_i=1,2,\mathrm{3....}P$

Wherein, n represents the node radar number in described radar network, and n>=2; represent the p that i-th node detections of radar in described n node radar arrives _ithe complex magnitude of the echo data of individual point target; be a matrix, line number is all umber of pulses in each Coherent processing cycle, and columns is the number P of point target.

3. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 2 comprises following sub-step:

Described radar network 2a) is established to comprise n node radar, wherein n>=2, if the echo data of point target comprises P point target after described matched filtering, from described n node radar, choose i-th node radar, and choose the p that described i-th node detections of radar arrive _ithe complex magnitude of the echo data of individual point target

2b) according to the p that described i-th node detections of radar arrives _ithe complex magnitude of the echo data of individual point target by point target p described in following formulae discovery _iin the estimated value of the average power of i-th node radar

${\mathrm{\ζ}}_{p_i,i}^2=\frac{{A_{p_i}^i}^H{A}_{p_i}^i}{Q},i=1,2,3,\mathrm{...},n$

Wherein, Q is the conjugate transpose of the number of PRT in the Coherent processing cycle, H representing matrix.

4. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 3 comprises following sub-step:

Described radar network 3a) is established to comprise n node radar, wherein n>=2, if the echo data of point target comprises P point target after described matched filtering; From described n node radar, choose i-th node radar and a jth node radar, from a described P point target, choose p point target, for described i-th node radar, described p point target is p _iindividual point target; For a described jth node radar, described p point target is p _jindividual point target, by the p of described i-th node detections of radar _ithe random complex envelope sequence of slow time of individual point target with the p of a jth node detections of radar _jthe random complex envelope sequence of slow time of individual point target combine, form envelope group;

3b) by described in following formulae discovery with the related coefficient of the envelope group formed

i≠j，i＝1，2，3…n，j＝1，2，3…n

Wherein, the conjugate transpose of H representing matrix;

3c) repeat 3b) to the related coefficient obtaining each envelope group.

5. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 4 comprises following sub-step:

According to the related coefficient of each envelope group its real part is chosen as relativity measurement corresponding to each envelope group by following formula

${\mathrm{\μ}}_{p_i,p_j}=real\left({\widehat{\mathrm{\ρ}}}_{p_i,p_j}\right)$

Wherein, real () represents right get real part.

6. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 5 comprises following sub-step:

5a) the real goal probability of miscarriage of justice P of given radar network _l;

5b) according to the real goal probability of miscarriage of justice P of described radar network _l, by the inspection thresholding of calculation of correlation corresponding to following formulae discovery each envelope group

${\mathrm{\ξ}}_{p_i,p_j}=\sqrt{{\mathrm{Q\ζ}}_{p_i,i}^2{\mathrm{\ζ}}_{p_j,j}^2/2}\·{\mathrm{\Φ}}^{-1}(1-{\left(1-P_l\right)}^{1/P})$

Wherein, Φ () represents standardized normal distribution, and Q is the number of PRT in the Coherent processing cycle, and P detects the target number obtained, represent point target p _iin the average power of i-th node radar and a jth node radar.

7. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 6 comprises following sub-step:

6a) by described relativity measurement with described inspection thresholding compare;

6b) when time, judge that the envelope group that described relativity measurement is corresponding is checked by decoy;

6c) when time, judge that the envelope group that described relativity measurement is corresponding is not checked by decoy, and two corresponding for this envelope group point targets are demarcated as decoy.

8. the anti-Deceiving interference method of radar network merged based on signal level according to claim 1, it is characterized in that, described step 7 comprises following sub-step:

7a) search the relativity measurement that the envelope group at two the point target places being demarcated as decoy is corresponding;

7b) complex magnitude of point target echo data corresponding for described relativity measurement is set to zero.