Passive radar networked signal cooperative complementary sorting method and system

Abstract

The application discloses a passive radar network signal cooperative complementary sorting method and system, wherein the method comprises the following steps: cooperatively complementing mixed alias radiation source pulse sequence signals received by multiple passive radar stations to obtain complemented pulse sequence signals; and sorting the complemented pulse sequence signals by using a cumulative difference histogram method to separate pulse sequences of different radiation sources. Compared with an existing single-station passive radar signal sorting method, the method solves the problem of incomplete pulse sequences caused by pulse loss, greatly improves the correctness of signal sorting, and has better sorting performance, especially when pulse loss is serious.

Description

Technical Field

[0001] This invention belongs to the field of passive radar technology, specifically relating to a passive radar network signal cooperative complementary sorting method and system. Background Technology

[0002] Passive radar, also known as passive radar, does not radiate electromagnetic waves itself, but directly utilizes the energy radiated by the target to detect its location. Signal sorting from the radiation source is a crucial component of passive radar network operations, and a necessary prerequisite and foundation for radiation source localization and subsequent work. Only after successfully sorting aliased signals can the next steps of localization and tracking be performed.

[0003] In the current field of signal sorting, both domestic and international methods for sorting radiation source signals mainly fall into two categories: sorting methods based on the Pulse Repetition Interval (PRI) and sorting methods based on multi-parameter feature fusion. PRI-based sorting methods primarily include statistical histogram methods, cumulative difference histogram methods, sequence difference histogram methods, and PRI transform methods. Multi-parameter fusion methods include clustering-based signal sorting methods and neural network-based signal sorting methods. Neural network-based sorting methods transform the radiation source signal sorting problem into an image recognition problem. However, current research on both PRI-based and multi-parameter feature fusion methods primarily focuses on the rapid and accurate estimation of the PRI of the radiation source target signal, or on innovations in classification algorithms, without considering the impact of pulse loss on sorting in real-world environments.

[0004] Regarding pulse loss, domestic scholar Yang Cui analyzed the main causes of this phenomenon, including: loss due to unmet signal reception conditions, loss caused by simultaneous arrival of signals, loss during receiver recovery time, loss due to improper signal processing and sorting, and loss of low repetition rate signals due to an insufficient buffer. Based on these reasons, relevant scholars have proposed some targeted solutions. For example, Wang Huijuan et al. proposed a method for PRI estimation based on the greatest common divisor with tolerance under high pulse loss rates, achieving a high signal sorting success rate even with severe pulse loss.

[0005] However, most existing methods are based on algorithmic innovations on the basis of monostation passive radar. In the actual signal sorting process, monostation passive radar will suffer from reduced signal sorting performance, low accuracy, or even signal sorting failure due to pulse loss. Summary of the Invention

[0006] To address the signal sorting performance degradation of a single passive radar receiver station due to pulse loss in existing technologies, this invention provides a passive radar network signal cooperative and complementary sorting method and system. The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] In a first aspect, the present invention provides a passive radar network signal cooperative complementary sorting method, comprising:

[0008] The pulse sequence signals of aliased radiation sources received by multiple passive radar stations are coordinated and complemented to obtain complementary pulse sequence signals;

[0009] The cumulative difference histogram method is used to sort the complementary pulse sequence signals to separate pulse sequences from different radiation sources.

[0010] Secondly, the present invention provides a passive radar network signal cooperative complementary sorting system, comprising:

[0011] The cooperative complementarity module is used to cooperatively complement the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain a complementary pulse sequence signal.

[0012] The signal sorting module is used to sort the complementary pulse sequence signals using the cumulative difference histogram method to separate pulse sequences from different radiation sources.

[0013] The beneficial effects of this invention are:

[0014] This invention is based on the characteristic that, within a certain timeframe, the data received by each passive radar station in a passive radar network have the same representation of the current environment, and that the information between the passive radar receiving stations is complementary. It proposes a cooperative signal sorting method for passive radar networks. This method uses cooperative complementary processing to complement the signal information of the same radiation source among all passive radar receiving stations, obtaining relatively complete radiation source information through data fusion. Then, sorting techniques are used to separate the pulse sequences of different radiation sources. Compared to existing single-station passive radar signal sorting methods, this method effectively solves the problem of incomplete pulse sequences caused by pulse loss, greatly improving the accuracy of signal sorting, especially when pulse loss is severe, exhibiting better sorting performance.

[0015] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a passive radar network signal cooperative complementary sorting method provided in an embodiment of the present invention;

[0017] Figure 2This is a flowchart illustrating the implementation of the cumulative difference complementary algorithm provided in this embodiment of the invention.

[0018] Figure 3 This is a flowchart illustrating the implementation of the instantaneous cooperative correlation complementarity algorithm provided in this embodiment of the invention;

[0019] Figure 4 This is a structural block diagram of a passive radar network signal cooperative complementary sorting system provided in an embodiment of the present invention;

[0020] Figure 5 This is a simulation experiment showing the sorting results of signals from multiple radiation sources by a single passive radar receiving station using the cumulative difference histogram method.

[0021] Figure 6 This is a simulation experiment showing the results of radar network collaborative sorting using the cumulative difference complementary method of this invention.

[0022] Figure 7-9 This is a simulation experiment showing the results of sorting the received aliased radiation source pulse sequence signals by individual radar receiving stations a, b, and c under different pulse loss conditions.

[0023] Figure 10-12 The peak spectra of the received aliased radiation source pulse sequence signals from individual radar receiving stations a, b, and c in the simulation experiment are obtained by using the correlation matching method in the instantaneous cooperative correlation complementation method.

[0024] Figure 13 It is the peak spectrum after instantaneous coordinated correlation and complementation of stations a, b, and c in the simulation experiment;

[0025] Figure 14 This is the cumulative difference histogram sorting result after instantaneous co-correlation complementation in the simulation experiment. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0027] Example 1

[0028] In a passive radar network, the information received by each passive radar receiver is correlated and complementary within a fixed time period. Therefore, by utilizing the correlation and complementarity of the information received by each passive radar receiver and through a certain fusion method, the received aliased radiation source pulse sequence can be determined and made complete.

[0029] Based on this, the present invention designs a passive radar network signal cooperative complementary sorting method. Please refer to [link to relevant documentation]. Figure 1 , Figure 1This is a flowchart illustrating a passive radar network signal cooperative complementary sorting method provided in an embodiment of the present invention. The method includes:

[0030] Step 1: Perform coordinated complementation on the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain the complementary pulse sequence signal;

[0031] Step 2: Use the cumulative difference histogram method to sort the complementary pulse sequence signals to separate the pulse sequences of different radiation sources.

[0032] This embodiment proposes a passive radar network signal collaborative sorting method based on the characteristics that, within a certain time period, the data received by each passive radar station in a passive radar network have the same representation of the current environment, and the information between the passive radar receiving stations is complementary. This method uses collaborative complementary processing to complement the signal information of the same radiation source among all passive radar receiving stations, obtaining relatively complete radiation source information through data fusion. Then, sorting techniques are used to separate the pulse sequences of different radiation sources. Compared to existing single-station passive radar signal sorting methods, this method effectively solves the problem of incomplete pulse sequences caused by pulse loss, greatly improving the accuracy of signal sorting, especially when pulse loss is severe, exhibiting better sorting performance.

[0033] Example 2

[0034] Based on the above embodiment 1, this embodiment proposes a cumulative difference complementarity method to address the situation where different distances between the radiation source target and the passive radar network result in different sequences of aliased pulses received by each receiving station. This method is mainly used for signal complementarity when the sequence difference of aliased signals is small, which effectively solves the problem of incomplete pulse sequences caused by pulse loss, thereby greatly improving the performance of signal sorting.

[0035] The cumulative difference complementarity method proposed in this embodiment will be described in detail below.

[0036] Step 1: Perform cumulative difference complementation on the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain the cumulative difference complemented pulse sequence signal.

[0037] Please see Figure 2 , Figure 2 If the cumulative difference complementary algorithm implementation flow provided in this embodiment of the invention is as follows, then step 1 specifically includes:

[0038] 11) The cross-correlation method is used to perform cross-correlation time registration on the aliased radiation source pulse signals received from different passive radar stations in order to align the different aliased radiation source pulse signals and obtain the aligned aliased radiation source pulse signals.

[0039] Specifically, the cross-correlation method is used to find the point of maximum correlation between the signals from the two passive radar receivers, and the alignment time is calculated for time registration. Assuming that the signals emitted by each radiation source are pulse signals, taking a linear frequency modulated signal as an example, the form of the signal from the i-th radiation source is:

[0040] S i (t)=A i exp(jπK i t 2 )+w i i = 1, ..., N r ;

[0041] Where, N r A represents the number of passive radar receiving stations. i Let K be the signal amplitude of the i-th radiation source signal. i w is the tuning frequency of the linear frequency modulated signal. i The noise is a zero-mean complex Gaussian white noise, transmitted independently from different radiation sources. Taking two passive radar receiving stations, a and b, as examples, the aliased signal received by station a from multiple radiation sources is as follows:

[0042]

[0043] The aliased signals from multiple radiation sources received by Bilibili are:

[0044]

[0045] Where Δt is the registration time; then the cross-correlation of the signals from the two stations is:

[0046]

[0047] in, This represents the convolution operation.

[0048] This embodiment finds the spike by cross-correlation of the two station signals, aligns the two signals, and facilitates the next step of cumulative difference complementary sorting.

[0049] 12) Based on the likelihood comparison alignment, attribute association is performed on the aliased radiation source pulse signals, and pulse signals originating from the same radiation source are identified as pulse signals to be complementary.

[0050] This embodiment uses the likelihood ratio to derive the attribute association threshold under a certain probability of misjudgment. It is understandable that after time registration, before complementation, it is necessary to confirm whether the corresponding pulses received by each passive radar receiving device originate from the same radiation source.

[0051] First, based on the frequency and measurement difference of the pulse sequence signal, calculate the probability P(y|H0) of event H0 and the probability P(y|H1) of event H1; where y represents the measurement difference of the complementary pulse signal, event H0 indicates that the two pulses belong to the same radiation source, and event H1 indicates that the two pulses do not belong to the same radiation source.

[0052] Specifically, taking two pulses to be complemented from two passive radar receiving stations, a and b, as an example, the pulse measurement values ​​are taken as signal frequencies. Let the signal frequencies of the two pulses to be complemented be f. a f b :

[0053] f a =f a '+Δf a f b =f b '+Δf b ;

[0054] Where f' represents the true frequency, Δf represents a mean of 0, and σ represents the variance. 2 Gaussian distribution measurement error. Let event H0 represent two pulses belonging to the same radiation source, i.e., f... a '=f b ', Event H1 is that the two pulses do not belong to the same radiation source, i.e., f a '-f b '≠0, y is the difference between the measured values, y n =f an -f bn , n = 1, 2, ..., N, y n Let H represent the difference in the nth group of measurements. There are N groups in total. Then the probability of event H0 occurring is:

[0055]

[0056] Because in H0, μ1 = μ2, therefore:

[0057]

[0058] Among them, μ1, μ2, and f a f b The mean and variance of . Similarly, the probability of event H1 occurring is:

[0059]

[0060] Then, the likelihood ratio Λ(y) is defined, and its expression is:

[0061]

[0062] Next, based on the likelihood ratio, the misjudgment probability P is calculated using the Neyman-Pearson criterion. F And obtain the threshold value T under different false positive probabilities. t .

[0063] Specifically, according to the Neyman-Pearson criterion η t To achieve the optimal ratio, therefore:

[0064]

[0065] Then its log-likelihood ratio is:

[0066]

[0067] We can obtain:

[0068]

[0069] Where γ(y) is the sufficient statistic; knowing the sufficient statistic is equivalent to knowing y, T. t The threshold values ​​for different false positive probabilities are expressed as follows:

[0070]

[0071]

[0072] Let the probability of misclassification be P. F Its physical meaning is: the probability that two objects belonging to the same radiation source are judged to not belong to the same radiation source. P F The expression should be:

[0073]

[0074] Where erf(x) is the error function, which can be obtained by looking up a table, and its expression is:

[0075]

[0076] Given a misclassification probability P F Then, the threshold value T can be obtained by looking up a table. t .

[0077] The measurement difference y of the pulse signals to be complemented is compared with the threshold value T. t For comparison, if y <T t If the event H0 is true, then the two pulses belong to the same radiation source; otherwise, the event H1 is true, meaning the two pulses do not belong to the same radiation source.

[0078] Finally, pulse signals originating from the same radiation source are identified as the complementary pulse signals.

[0079] 13) Compare the measurement difference between the current complementary pulse signal and the threshold, and perform data-level complementation on the pulse signals that have passed the threshold at the same time.

[0080] 14) The pulses from the same radiation source of each individual passive radar receiving device are complemented and arranged into a complete pulse sequence to obtain the pulse sequence signal after cumulative difference complementation.

[0081] Step 2: Use the cumulative difference histogram method to sort the pulse sequence signals after the cumulative difference is complemented, in order to separate the pulse sequences of different radiation sources. Specifically, this includes:

[0082] 21) Based on the time interval between adjacent pulses of the pulse sequence signal after cumulative difference complementation, statistically analyze and plot a first-order difference histogram;

[0083] 22) When the statistical value of the first-level difference histogram exceeds a preset threshold, calculate the second-level difference histogram; if the statistical values ​​of both the first-level difference histogram and the second-level difference histogram exceed the preset threshold, it is determined that PRI may exist;

[0084] 23) Based on the above PRI, search in the pulse sequence signal after the cumulative difference is complemented. If successful, the search sequence is removed; if unsuccessful, continue to perform secondary difference histogram statistics until the search is successful, and so on.

[0085] 24) Perform first-level difference histogram statistics on the pulse sequence signals after cumulative difference complementation of the removed sequences, and repeat the search until no difference histogram exceeds the threshold, thereby separating the pulse sequences of different radiation sources.

[0086] The cumulative difference complementarity method provided in this embodiment can achieve signal synergy complementarity when the received aliased signals have small order differences, thereby improving the signal sorting performance.

[0087] Example 3

[0088] When the radiation source target is close to the passive radar network, the aliased signals received by each passive radar receiving station are disordered and complex pulse sequences. At this time, pulse complementation is more complicated and cannot be achieved by simply time registration and attribute association.

[0089] Based on this, and building upon the first embodiment described above, this embodiment proposes an instantaneous cooperative correlation complementary method to achieve complementary cooperative sorting even when the aliasing signals received by each passive radar receiving device in a passive radar network are in very different order.

[0090] The instantaneous cooperative correlation complementarity method proposed in this embodiment will be described in detail below.

[0091] Step 1: Perform instantaneous cooperative correlation complementation on the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain the pulse signal after instantaneous cooperative correlation complementation.

[0092] Please see Figure 3 , Figure 3 The flowchart of the instantaneous cooperative correlation complementary algorithm provided in this embodiment of the invention is as follows: Step one specifically includes:

[0093] 1a) Select the received signal of a certain passive radar receiving station as a reference signal, and select a pulse in the reference signal as a reference pulse.

[0094] 1b) Based on the correlation matching method, the reference pulse is autocorrelated with other pulses of the reference signal, and the reference pulse is cross-correlated with all pulses of the signals received by other passive radar stations to obtain the position of the peak pulse that best matches the reference pulse.

[0095] Specifically, let the aliased signal received by the reference passive radar receiver be s0(t), and the reference pulse signal be x(t). Then, the autocorrelation of the reference pulse with respect to the reference receiver is as follows:

[0096]

[0097] Where R(τ') is the autocorrelation function and T is the observation time, its meaning is: within a certain observation time, the value of τ' is continuously changed, and the two signals are multiplied and accumulated point by point within a certain range. The area of ​​the integral is a value of R. This is repeated continuously, and finally the position of the pulse that best matches the reference pulse can be known by the peak value of the graph of the function R.

[0098] Let s be the aliasing signal received by the i-th passive radar receiver. i (t), then the cross-correlation of the reference pulse with the i-th receiving station is:

[0099]

[0100] Similarly, the peak value of the graph of the function R(τ') can be used to determine the pulse position that best matches the reference pulse.

[0101] 1c) Using the idea of ​​complementarity, the peak loss caused by the pulse loss is made up at the position of the peak pulse to obtain the pulse signal after instantaneous co-correlation and complementarity.

[0102] Step 2: The cumulative difference histogram method is used to sort the instantaneously co-correlated and complementary pulse signals to separate pulse sequences from different radiation sources. Specifically, this includes:

[0103] 2a) The second complete pulse sequence signal is sorted using the cumulative difference histogram method to select the signal that belongs to the same radiation source as the reference pulse.

[0104] 2b) Remove the successfully sorted signals and return to the step of instantaneous correlation complementation of the pulse sequence signals until all signals have been sorted.

[0105] For a detailed explanation of the cumulative difference histogram method, please refer to Embodiment 2 above or existing related technologies. This embodiment will not provide a detailed explanation here. After successfully sorted signals are removed, return to step 1a) to redetermine the reference signal and complement the spikes until all signals are sorted.

[0106] The instantaneous cooperative correlation complementarity method provided in this embodiment can achieve cooperative complementarity of signals when the received aliased signals have large differences in order, thereby improving the performance of signal sorting.

[0107] Example 4

[0108] Based on Embodiment 1 above, this embodiment provides a passive radar network signal cooperative complementary sorting system. Please refer to... Figure 4 , Figure 4 This is a structural block diagram of a passive radar network signal cooperative complementary sorting system provided in an embodiment of the present invention. The system includes:

[0109] The cooperative complementarity module is used to cooperatively complement the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain a complementary pulse sequence signal.

[0110] The signal sorting module is used to sort the complementary pulse sequence signals using the cumulative difference histogram method to separate pulse sequences from different radiation sources.

[0111] Furthermore, the collaborative complementarity module includes a time-cumulative difference complementarity unit and an instantaneous collaborative correlation complementarity unit;

[0112] The time cumulative difference complementation unit is used to perform cumulative difference complementation on the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain the cumulative difference complemented pulse sequence signal.

[0113] The instantaneous coordinated correlation complementation unit is used to perform instantaneous coordinated correlation complementation on the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain the instantaneous coordinated correlation complemented pulse signal.

[0114] The system provided in this embodiment can implement the methods provided in Embodiment 1, Embodiment 2, and Embodiment 3 above; for the detailed implementation process of the cumulative difference complementary sorting method, please refer to Embodiment 2 above, and for the detailed implementation process of the instantaneous cooperative correlation complementary method, please refer to Embodiment 3 above.

[0115] The system provided in this embodiment can also effectively solve the problem of incomplete pulse sequences caused by pulse loss, greatly improving the signal sorting performance of the system.

[0116] Example 5

[0117] The following simulation experiments verify and illustrate the beneficial effects of the passive radar networking signal cooperative complementary sorting method proposed in this invention.

[0118] 1. Simulation conditions

[0119] Three passive radar receiving stations (a, b, and c) were set up, and two fixed PRI (Primary Pathway) radiation source targets with pulse loss times of 180 μs and 220 μs were set in space. The distances of the targets from the passive radar receiving stations were determined, and signal complementation was performed using the cumulative difference complementation method and the instantaneous cooperative correlation complementation method proposed in this invention. The sorting results were then compared between single-station sorting under different pulse loss conditions at each passive radar receiving station and the sorting results using the cooperative complementation sorting algorithm of this invention.

[0120] The simulation software environment is an Intel(R) Core(TM) i7-6700 CPU@3.40GHz, and Matlab R2017a running Windows 7 Ultimate 64-bit operating system.

[0121] 2. Simulation Content and Result Analysis

[0122] Experiment 1:

[0123] When pulse loss reaches 30%, the cumulative difference histogram method is used to sort signals from multiple radiation sources at a single passive radar receiving station. The results are as follows: Figure 5 As shown. Simultaneously, for aliased radiation source pulse sequence signals with a 30% pulse loss, the cumulative difference complementarity method proposed in this invention is used for radar network collaborative sorting, and the results are as follows. Figure 6 As shown.

[0124] from Figure 5 and Figure 6 As can be clearly seen, the cumulative difference histogram effectively solves the harmonic problem. (Comparison) Figure 5 and Figure 6 It can be seen that when single-station sorting fails, the cumulative difference complementary method achieves successful signal sorting.

[0125] Experiment 2:

[0126] Under the above simulation conditions, when a single radar receiving station experiences pulse loss at different times, the received aliased radiation source pulse sequence signals are sorted, and the results are as follows: Figure 7-9 As shown. Among them, Figure 7 This is a sorting result diagram when the pulse loss at station A is 40%. Figure 8 The image shows the sorting results when the pulse loss rate at station B is 30%. Figure 9 The sorting results are shown in the diagram when the pulse loss at station C is 50%.

[0127] It can be seen that without using the instantaneous correlation complement method, the pulse signals of the three receiving stations a, b, and c failed to be successfully sorted due to pulse loss.

[0128] Experiment 3:

[0129] Under the above simulation conditions, station a is used as the reference receiving station, and the first pulse received by station a is used as the reference pulse for correlation matching. The correlation matching method in the instantaneous cooperative correlation complementarity method is used to perform correlation matching on the aliased radiation source pulse sequence signals received by individual radar receiving stations a, b, and c. The results are as follows: Figure 10-12 As shown, where, Figure 10 for Figure 7 When the corresponding pulse at station A is lost by 40%, the peak spectrum after complementation is obtained using the correlation matching method. Figure 11 for Figure 8 When the corresponding Bilibili pulse is lost by 30%, the complementary peak spectrum is obtained using the correlation matching method. Figure 12 for Figure 9 When the corresponding C-station pulse is lost by 50%, the complementary peak spectrum is obtained using the correlation matching method. Figure 10-12 The simulation results show that the peaks of the correlation matching method are also lost due to varying degrees of pulse loss at the three passive radar receiving stations a, b, and c.

[0130] Furthermore, Figure 13 The peak spectra of stations a, b, and c after instantaneous co-correlation complementation were generated. Figure 14 This is a histogram of the cumulative difference sorting results after instantaneous co-correlation and complementation.

[0131] from Figure 13 and Figure 14 It can be seen that after instantaneous co-correlation complementation, the lost spikes of each single station are made up, and the instantaneous co-correlation complementation method can solve the problem of single-station sorting failure caused by pulse loss.

[0132] This verifies that the network-based collaborative sorting method proposed in this invention can solve the problem of poor sorting performance of a single passive radar receiving station when pulses are severely lost.

[0133] This invention utilizes the fact that, within a certain timeframe, data received by various passive radar stations in a passive radar network share a common representation of the current environment, and the information from each passive radar receiving station is complementary. Based on this characteristic, a collaborative signal sorting technology for radiation sources sorts signals. Through collaborative processing, signal information from the same radiation source is made complementary across all passive radar receiving stations, resulting in complete radiation source information through data fusion. Compared to traditional single-station signal sorting methods, this technology offers better sorting performance even with severe pulse loss.

[0134] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A passive radar network signal cooperative complementary sorting method, characterized in that, include: The method involves performing cooperative complementation on aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain a complementary pulse sequence signal; this includes: performing cumulative difference complementation on aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain a cumulative difference complementary pulse sequence signal; or performing instantaneous cooperative correlation complementation on aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain an instantaneous cooperative correlation complementary pulse signal. The cumulative difference histogram method is used to sort the complementary pulse sequence signals to separate pulse sequences from different radiation sources; The process involves accumulating and complementing the aliased radiation source pulse sequence signals received from multiple passive radar stations to obtain the accumulated difference complemented pulse sequence signal, including: Cross-correlation method is used to perform cross-correlation time registration on the aliased radiation source pulse signals received from different passive radar stations in order to align the different aliased radiation source pulse signals and obtain the aligned aliased radiation source pulse signals. Based on the likelihood ratio, attribute association is performed on the aligned aliased radiation source pulse signals, and pulse signals originating from the same radiation source are identified as pulse signals to be complementary. By comparing the measured difference between the current pulse signal to be complemented and the threshold, pulse signals that have passed the threshold at the same time are complemented at the data level. The pulses from each individual passive radar station originating from the same radiation source are complemented and arranged into a complete pulse sequence to obtain the pulse sequence signal after cumulative difference complementation. Instantaneous cooperative correlation complementation is performed on the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain the instantaneously cooperatively correlated and complemented pulse signals, including: The received signal of a certain passive radar station is selected as the reference signal, and a pulse in the reference signal is selected as the reference pulse; Based on the correlation matching method, the reference pulse is autocorrelated with other pulses of the reference signal, and the reference pulse is crosscorrelated with all pulses of the signals received by other passive radar stations to obtain the position of the peak pulse that best matches the reference pulse. By employing the concept of complementarity, the peak loss caused by the pulse loss is compensated at the position of the peak pulse to obtain the pulse signal after instantaneous co-correlation and complementarity.

2. The passive radar network signal cooperative complementary sorting method according to claim 1, characterized in that, Based on likelihood comparison, attribute association is performed on the aligned aliased radiation source pulse signals to identify pulse signals originating from the same radiation source as to be complementary pulse signals, including: Calculate the event based on the frequency and measurement difference of the pulse sequence signal. probability of occurrence and events probability of occurrence Among them, the event This indicates that the two pulses belong to the same radiation source, and the event... This indicates that the two pulses do not belong to the same radiation source. This represents the measurement difference between the complementary pulse signals; The likelihood ratio is defined as follows: ； Based on the likelihood ratio, the false positive probability is calculated using the Neyman-Pearson criterion. And obtain threshold values ​​under different false positive probabilities. ; The measurement difference of the complementary pulse signals With the threshold value In comparison, if Then the event If true; otherwise, the event Established; Pulse signals originating from the same radiation source are identified as complementary pulse signals.

3. The passive radar network signal cooperative complementary sorting method according to claim 2, characterized in that, The cumulative difference histogram method is used to sort the complementary pulse sequence signals to separate pulse sequences from different radiation sources, including: Based on the time interval between adjacent pulses of the pulse sequence signal after the cumulative difference is complemented, a first-level difference histogram is statistically analyzed and plotted. When the statistical value of the first-level difference histogram exceeds a preset threshold, the second-level difference histogram is calculated; if the statistical values ​​of both the first-level difference histogram and the second-level difference histogram exceed the preset threshold, it is determined that PRI may exist. Based on the PRI, a search is performed on the pulse sequence signal after the cumulative difference is complemented. If successful, the search sequence is removed; if unsuccessful, the second-level difference histogram statistics are continued until the search is successful, and so on. After the cumulative difference is complemented, the pulse sequence signal after the elimination sequence is re-performed by the first-level difference histogram statistics, and the search is repeated until no difference histogram exceeds the threshold, thereby separating the pulse sequences of different radiation sources.

4. The passive radar network signal cooperative complementary sorting method according to claim 1, characterized in that, The cumulative difference histogram method is used to sort the complementary pulse sequence signals to separate pulse sequences from different radiation sources, including: The pulse signals after instantaneous co-correlation and complementation are sorted using the cumulative difference histogram method to separate signals that belong to the same radiation source as the reference pulse. The successfully sorted signals are discarded, and the process returns to the step of instantaneous co-correlation complementation of the pulse sequence signals until all signals are sorted out, thereby separating the pulse sequences of different radiation sources.

5. A passive radar network signal cooperative complementary sorting system, used to implement the method of claim 1, characterized in that, include: The cooperative complementarity module is used to cooperatively complement the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain a complementary pulse sequence signal. The signal sorting module is used to sort the complementary pulse sequence signals using the cumulative difference histogram method to separate pulse sequences from different radiation sources.

6. A passive radar network signal cooperative complementary sorting system according to claim 5, characterized in that, The collaborative complementarity module includes a time cumulative difference complementarity unit and an instantaneous collaborative correlation complementarity unit; The time cumulative difference complementation unit is used to perform cumulative difference complementation on the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain the cumulative difference complemented pulse sequence signal. The instantaneous coordinated correlation complementation unit is used to perform instantaneous coordinated correlation complementation on the aliased radiation source pulse sequence signals received by multiple passive radar stations to obtain the instantaneous coordinated correlation complemented pulse signal.

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