Intermittent sampling and forwarding interference resisting method for adaptive distributed radar

Abstract

The invention relates to the technical field of radar signal processing, in particular to a self-adaptive distributed radar intermittent sampling and forwarding interference resisting method, which is used for accurately and stably detecting a real target when encountering intermittent sampling and forwarding interference and comprises the following steps of: performing signal-level interference alignment processing through echoes of a plurality of receiving stations; and realizing intra-pulse coherent-level intermittent sampling forwarding interference cancellation fusion processing and optimization under the optimal site combination by adopting a simulated annealing-particle swarm search sequence optimization algorithm. According to the method provided by the invention, by virtue of the characteristics of high power gain of intermittent sampling and forwarding interference and high correlation of homologous interference radiation, the adaptive distributed radar intermittent sampling and forwarding interference resisting method provided by the invention has relatively good robustness in the environment of resisting different types of intermittent sampling and forwarding interference and different interference-to-signal ratios; and the target accurate detection capability in an information battlefield confrontation environment is improved.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, specifically an adaptive distributed radar anti-intermittent sampling and forwarding interference method. Background Technology

[0002] Intermittent Sample-and-Relay Jamming (ISRJ) is a new type of coherent jamming that emerged with the development of Digital Radio Frequency Memory (DRFM). It can generate multiple false target spikes around the target in the echo signal, disrupting the radar's detection of the real target. Currently, the ISRJ equipped by self-defense jammers is gradually making monostatic radars less effective in anti-jamming target detection due to its fast response speed and small difference from radar.

[0003] Currently, methods for suppressing multiple false target interference (MPDI) in multi-static radar networks (MSRNs) belonging to the ISRJ can be mainly divided into two categories: data-level and signal-level. Data-level MPDI suppression methods mainly utilize the non-cooperative nature of false targets, identifying MPDI interference through information inconsistencies in their spatial position and velocity. However, in environments where the echo power of real targets is weak, this type of method is prone to losing the ability to detect real targets.

[0004] To counter defensive deception jamming, existing technologies propose two solutions: First, a cooperative cancellation method based on monostatic radar networking. This method utilizes any difference in amplitude, phase, delay, or Doppler shift between the target and the jamming signals received by the two receiving stations to cancel the jamming, preserving the target echo signal to the greatest extent possible. Second, by setting up dedicated node radars aligned with the jamming source, sampling the jamming signal, and using its correlation to achieve cancellation. However, sampling the jamming signal is challenging when dealing with defensive or accompanying jamming.

[0005] There are still some problems with the multi-receiving-station cooperative anti-interference algorithm that need to be improved: (1) Data-level methods are difficult to solve the target detection problem when the real target is covered by noise / interference; (2) Most signal-level methods require the near-field area conditions to be met. In the far-field area, the echo correlation of each receiving station is small, and the target echo energy loss is large after cancellation; (3) Signal-level methods mainly use the cancellation result of the echo signals of two stations and do not fully consider the fusion gain of the echo signals in the environment of more stations. Summary of the Invention

[0006] To address the challenge of leveraging the fusion gain of multiple receiver stations in low signal-to-noise ratio and far-field scenarios, this invention proposes an adaptive distributed radar anti-intermittent sampling and forwarding interference method.

[0007] To solve the above problems, the present invention adopts the following technical solution: An adaptive distributed radar anti-interference method for intermittent sampling and forwarding: S1. Perform signal-level interference alignment processing on the echoes from multiple receiving stations, including aligning the interference delay, interference phase, and interference amplitude of the echo signals from multiple receiving stations. S2. The simulated annealing-particle swarm search sequence optimization algorithm is used to realize the interference cancellation and fusion processing and optimization of the pulse coherent level intermittent sampling forwarding under the optimal site combination. This includes interference coherent cancellation between two receiving stations, generating candidate fusion signals by prefix sum operation according to the signal-to-noise ratio contribution, evaluating and screening the candidate fusion signals, executing the initialization and iteration process of the sequence optimization simulated annealing-particle swarm search algorithm, and outputting the final fusion signal corresponding to the globally optimal sequence.

[0008] Furthermore, the interference alignment process includes using the echo signal from the first receiving station as a reference signal. For the echo signals of the other receiving stations Alignment is then performed.

[0009] Furthermore, S1 includes S1.1, interference delay alignment including calculating the reference signal. Compared with the echo signals from other receiving stations The relevant functions are shown in equation (1): (1); In the formula, The echo signal from the first receiving station. For the conjugate operation of complex numbers, For convolution operations, The value of the echo signal from the m-th receiving station at discrete time -k after conjugation and time reversal. No. The time delay difference between the interference of the receiving station signal and the interference of the reference signal. Depend on The maximum value is obtained as shown in equation (2): (2); In the formula, Let be the value of the cross-correlation function between the echo signal from the first receiving station and the echo signal from the m-th receiving station at discrete time k. For mold taking operation; when The formula for calculating the signal after interference delay alignment at the m-th receiving station is as follows: As in equation (3): (3); In the formula, Let be the echo signal from the m-th receiving station, after the first processing, and its value at discrete time n. The echo signal of the m-th receiving station in discrete time The value at; when The formula for calculating the signal after interference delay alignment at the m-th receiving station is as follows: As in equation (4): (4).

[0010] Furthermore, S1 includes S1.2, interference phase alignment and phase difference resolution, as shown in equation (5): (5); In the formula, Let be the phase difference between the echo signal from the first receiving station and the echo signal from the m-th receiving station. For phase extraction, The value of the echo signal from the first receiving station at discrete time n after the first processing. The value of the echo signal from the m-th receiving station at discrete time -n after the first processing, conjugate operation, and time reversal. Phase compensation is performed based on the phase difference in equation (5), as shown in equation (6): (6); In the formula, Let be the value of the echo signal from the m-th receiving station at discrete time n after the second processing.

[0011] Further, S1 includes S1.3, interference amplitude alignment, which includes utilizing the high-power characteristics of intermittent sampling forwarding interference, in the time domain of the echo signal, locating the interference signal through a dual sliding window bidirectional constant false alarm rate detector, determining the range of the target echo and interference and estimating the amplitude of the interference, and then aligning the interference amplitudes of the remaining receiving station signals with the reference signal, as shown in equation (7): (7); In the formula, Let be the value of the echo signal from the m-th receiving station at discrete time n after the third processing. The interference amplitude of the first receiving station. Let be the interference amplitude of the m-th receiving station.

[0012] Further, S2 includes S2.1, interference coherent cancellation between receiving stations, including interference coherent cancellation between the first receiving station and the second receiving station up to the Mth receiving station, the second receiving station and the third receiving station up to the Mth receiving station, ..., the (M-1)th receiving station and the Mth receiving station, and the cancellation result vector. The calculation of elements is as follows, as shown in equation (8): (8); In the formula, , Let be the value of the echo signal from the i-th receiving station at discrete time n after the third processing. Let be the value of the echo signal from the j-th receiving station at discrete time n after the third processing.

[0013] Furthermore, S2 includes S2.2, candidate fusion signal. The calculation, The signal-to-noise ratio contributions of each cancellation result are sorted from largest to smallest, and then... Prefix sum operation generates candidate fusion signals As in equation (9): ; In the formula, , Let be the value of the j-th cancellation result signal at discrete time n.

[0014] Furthermore, S2 includes S2.3, evaluation and screening of candidate fusion signals, with the evaluation function as shown in equation (10). (10); (11); (12); In the formula, Candidate fusion signal after pulse compression , To detect the signal-to-noise ratio of the target, for Number of targets after constant false alarm rate detection Whether it is 1, as shown in equation (11), is determined by calculating the 3dB power of the target main lobe. Maximum power at other points The signal-to-noise ratio of the detected target in the candidate fused signal after pulse compression is obtained. , as in equation (12).

[0015] Furthermore, S2 includes S2.4, the speed update during the initialization and iteration process of the sequence-optimized simulated annealing-particle swarm search algorithm, setting the algorithm's initial temperature to the current temperature. P random receiver station sequences are generated as P particles. The initial sequence for the p-th particle is given, and each particle is randomly generated with no more than 3 exchange operators as initial velocities. Then, the algorithm iterates through all particles and performs the following operations, updating the velocity of particle p according to equation (13): (13); In the formula, For the first The first particle The speed of the next iteration For the first The sequence of particles at time t-1 This is the particle's own historical optimal sequence. This is the historical optimal sequence for all particles. For sequence Transform to sequence speed, and for The probability value within, For probability values Each commutator in the retention action's velocity, For probability values Each commutator in the retention action's velocity, This is to combine all the exchange operators for velocities a and b.

[0016] Furthermore, the iterative process also includes a sequence update and acceptance judgment process for the sequence optimization simulated annealing-particle swarm search algorithm, for particles The sequence is updated according to formula (14): (14); In the formula, Let be the sequence of particles at time t. Let be the updated particle velocity at time t. For position Execution speed All commutation operators in the middle; The evaluation value of the particle sequence is assessed using equation (10), and the updated particles are determined according to the Metropolis criterion, as shown in equation (15):

[0017] (15); In the formula, for The probability value is randomly generated internally. The difference between the new and old sequence evaluation values ​​of the particles is considered. If the result is unacceptable, the particle's velocity and sequence are updated again until the maximum number of iterations is reached. Accepted later; After traversing all particles, update the globally optimal sequence. and the historical optimal sequence of each particle Then proceed to the next iteration, until the maximum number of iterations is reached. Then stop and output the globally optimal sequence. The corresponding final fusion signal .

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes an adaptive distributed multi-station cancellation fusion method by leveraging the high power gain of ISRJ interference and the high correlation of co-source interference radiation. The proposed algorithm can improve the signal-to-interference ratio (SJR) after suppression by more than 10 dB compared with the optimal two-station cancellation method. It also has good robustness against different types of ISRJ and different interference-to-signal ratio (JSR) environments, thereby improving the target accuracy detection capability and interference countermeasures level in future information battlefield confrontation environments. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the technical process of the present invention. Figure 2 The results for receiving station 4 are shown in the following diagrams: no intermittent sampling direct forwarding interference (ISDJ) echo (orange) and with ISDJ echo (blue); Figure 3 The results for receiving station 4 are shown in the images with and without ISRJ interference echoes (orange) and with ISRJ interference echoes (blue). Figure 4 The diagram shows the results of receiving station 4 under the conditions of no dual-channel complex modulation ISRJ (ISRJ-Complex) interference echo (orange) and with ISRJ-Complex interference echo (blue); Figure 5 The graph shows the effect of Least Mean Square (LMS) algorithm on suppressing ISDJ interference. Figure 6 The diagram shows the effect of the optimal two-station cancellation method (ACTR) on suppressing ISDJ interference. Figure 7 The graph shows the effect of the sequence-free optimization method on suppressing ISDJ interference. Figure 8 The diagram illustrates the effectiveness of adaptive distributed radar jamming cancellation and multi-station fusion in suppressing ISDJ interference. Figure 9 The image shows the LMS anti-ISRJ interference suppression effect. Figure 10 The diagram shows the ACTR's effect on suppressing ISRJ interference. Figure 11 The graph shows the effect of the sequence-free optimization method on suppressing ISRJ interference. Figure 12 The diagram shows the effect of adaptive distributed radar jamming cancellation and multi-station fusion in suppressing ISRJ interference. Figure 13 The image shows the LMS's effectiveness in suppressing ISRJ-Complex interference. Figure 14 The diagram shows the ACTR's effect on suppressing ISRJ-Complex interference. Figure 15 The graph shows the effect of the sequence-free optimization method on suppressing ISRJ-Complex interference. Figure 16 The diagram shows the effect of adaptive distributed radar interference cancellation and multi-station fusion in suppressing ISRJ-Complex interference. Figure 17 A statistical chart comparing the ISDJ jam suppression performance of four algorithms—LMS, ACTR, unordered optimization method, and adaptive distributed radar—in different JSR environments. Figure 18 A statistical chart comparing the suppression performance of four algorithms—LMS, ACTR, sequence-free optimization method, and adaptive distributed radar—on ISRJ interference in different JSR environments. Figure 19 A statistical chart comparing the suppression performance of four algorithms—LMS, ACTR, sequence-free optimization method, and adaptive distributed radar—on ISRJ-Complex interference in different JSR environments. Detailed Implementation

[0020] The present invention will be further illustrated below with reference to embodiments. These embodiments are for illustrative purposes only and are not intended to limit the invention in any way.

[0021] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0022] An adaptive distributed radar anti-intermittent sampling and forwarding interference method includes: S1. Perform signal-level interference alignment processing on the echoes from multiple receiving stations, including aligning the interference delay, interference phase, and interference amplitude of the echo signals from multiple receiving stations. S2. The simulated annealing-particle swarm search sequence optimization algorithm is used to realize the intra-pulse coherent level intermittent sampling forwarding interference cancellation fusion processing and optimization under the optimal site combination. This includes coherent interference cancellation between two receiving stations, generating candidate fusion signals by prefix sum operation according to the signal-to-noise ratio (SNR) contribution, evaluating and screening the candidate fusion signals, executing the initialization and iteration process of the sequence optimization simulated annealing-particle swarm search algorithm, and outputting the final fusion signal corresponding to the globally optimal sequence.

[0023] Interference alignment processing includes using the echo signal from the first receiving station as a reference signal. For the echo signals of the other receiving stations Alignment is then performed.

[0024] S1 includes S1.1, and interference delay alignment includes calculating the reference signal. Compared with the echo signals from other receiving stations The relevant functions are shown in equation (1): (1); In the formula, The echo signal from the first receiving station. For the conjugate operation of complex numbers, For convolution operations, The value of the echo signal from the m-th receiving station at discrete time -k after conjugation and time reversal. No. The time delay difference between the interference of the receiving station signal and the interference of the reference signal. Depend on The maximum value is obtained as shown in equation (2): (2); In the formula, Let be the value of the cross-correlation function between the echo signal from the first receiving station and the echo signal from the m-th receiving station at discrete time k. For mold taking operation; when The formula for calculating the signal after interference delay alignment at the m-th receiving station is as follows: As in equation (3): (3); In the formula, Let be the echo signal from the m-th receiving station, after the first processing, and its value at discrete time n. The echo signal of the m-th receiving station in discrete time The value at; when The formula for calculating the signal after interference delay alignment at the m-th receiving station is as follows: As in equation (4): (4).

[0025] S1 includes S1.2 and the phase difference of the interference phase alignment solution, as shown in equation (5): (5); In the formula, Let be the phase difference between the echo signal from the first receiving station and the echo signal from the m-th receiving station. For phase extraction, The value of the echo signal from the first receiving station at discrete time n after the first processing. The value of the echo signal from the m-th receiving station at discrete time -n after the first processing, conjugate operation, and time reversal. Phase compensation is performed based on the phase difference in equation (5), as shown in equation (6): (6); In the formula, Let be the value of the echo signal from the m-th receiving station at discrete time n after the second processing.

[0026] S1 includes S1.3, interference amplitude alignment, which includes utilizing the high power characteristics of intermittent sampling forwarding interference, in the time domain of the echo signal, using a dual sliding window bidirectional constant false alarm rate detector to locate the interference signal, determine the range of the target echo and interference and estimate the amplitude of the interference, and then align the interference amplitude of the remaining receiving station signals with the reference signal, as shown in equation (7): (7); In the formula, Let be the value of the echo signal from the m-th receiving station at discrete time n after the third processing. The interference amplitude of the first receiving station. Let be the interference amplitude of the m-th receiving station.

[0027] S2 includes S2.1, interference coherent cancellation between receiving stations, including interference coherent cancellation between the first receiving station and the second receiving station up to the Mth receiving station, the second receiving station and the third receiving station up to the Mth receiving station, ..., the (M-1)th receiving station and the Mth receiving station, and the cancellation result vector. The calculation of elements is as follows, as shown in equation (8): (8); In the formula, , Let be the value of the echo signal from the i-th receiving station at discrete time n after the third processing. Let be the value of the echo signal from the j-th receiving station at discrete time n after the third processing.

[0028] S2 includes S2.2 and candidate fusion signals. The calculation, The signal-to-noise ratio contributions of each cancellation result are sorted from largest to smallest, and then... Prefix sum operation generates candidate fusion signals As in equation (9): ; In the formula, , Let be the value of the j-th cancellation result signal at discrete time n.

[0029] S2 includes S2.3, evaluation and screening of candidate fusion signals, with the evaluation function as shown in equation (10). (10); (11); (12); In the formula, Candidate fusion signal after pulse compression , To detect the SNR of the target, for Number of targets after constant false alarm rate detection Whether it is 1, as shown in equation (11), is determined by calculating the 3dB power of the target main lobe. Maximum power at other points The SNR of the detected target in the candidate fused signal after pulse compression was obtained. , as in equation (12).

[0030] Since the ablation process is determined, The problem of SNR contribution ranking can be transformed into interference-aligned echo signals. The problem of arranging the stations in sequence. The optimal station sequence can guarantee that after cancellation... The results are sorted in descending order of SNR contribution. When the number of receiving stations is small, the optimal order is directly solved by traversing all sequences. When the number of receiving stations is large (e.g., ...), the optimal order is solved by traversing all sequences. , The real-time requirements prevent the radar system from traversing and searching the entire sequence. Therefore, the Simulated Annealing-particle Swarm Optimization (SA-PSO) sequence optimization algorithm is adopted to adapt the algorithm to the receiver sequence problem of multi-station cancellation fusion.

[0031] S2 includes S2.4, the initialization and iterative velocity update of the sequence-optimized simulated annealing-particle swarm search algorithm, and setting the algorithm's initial temperature. P random receiver station sequences are generated as P particles. The initial sequence for the p-th particle is given, and each particle is randomly generated with no more than 3 exchange operators as initial velocities. Then, the algorithm iterates through all particles and performs the following operations, updating the velocity of particle p according to equation (13): (13); In the formula, For the first The first particle The speed of the next iteration For the first The sequence of particles at time t-1 This is the particle's own historical optimal sequence. This is the historical optimal sequence for all particles. For sequence Transform to sequence speed, and for The probability value within, For probability values Each commutator in the retention action's velocity, For probability values Each commutator in the retention action's velocity, This is to combine all the exchange operators for velocities a and b.

[0032] The iterative process also includes sequence updates and acceptance criteria for the sequence optimization simulated annealing-particle swarm search algorithm, and the particle swarm optimization process. The sequence is updated according to formula (14): (14); In the formula, Let be the sequence of particles at time t. Let be the updated particle velocity at time t. For position Execution speed All commutation operators in the middle; The evaluation value of the particle sequence is assessed using equation (10), and the updated particles are determined according to the Metropolis criterion, as shown in equation (15): (15); In the formula, for The probability value is randomly generated internally. The difference between the new and old sequence evaluation values ​​of the particles is considered. If the result is unacceptable, the particle's velocity and sequence are updated again until the maximum number of iterations is reached. Accepted later; After traversing all particles, update the globally optimal sequence. and the historical optimal sequence of each particle Then proceed to the next iteration, until the maximum number of iterations is reached. Then stop and output the globally optimal sequence. The corresponding final fusion signal .

[0033] The embodiments of the present invention consist of two steps: S1. Based on the simulation experiment, the MSRN is set up with 7 radar devices, including 6 R-type silent receiving stations and 4 receiving stations being T / R-type transceiver radars. The transmitted waveform adopts linear frequency modulation (LFM). The target radar cross section (RCS) is sampled from the distributions U(1,2) and N(0,π) to measure the energy and phase response distribution of the echo signal from each station. 2 The remaining simulation parameters are shown in Table 1. The average SNR for each station is approximately 1 dB. The jammer can release three types of ISRJ interference: ISDJ, ISRJ, and ISRJ-Complex.

[0034] Table 1. Simulation Parameter Settings .

[0035] Figure 2 , Figure 3 , Figure 4 This is a diagram showing the results of the interference-free echo and the interference-affected echo at receiving station 4 under different interference conditions. ISRJ-Complex is a dual-channel composite interference, and the sampling width, sampling period, and frequency shift modulation of the interference component in each channel are random. The algorithm performance index is the SJR of the output suppressed signal pulse compression result, which is calculated as shown in equation (16): (16); In the formula, For the true target power, To determine the maximum power of the interference spike.

[0036] S2. The experiment selected LMS and ACTR as multi-station cancellation comparison algorithms, and also compared the fusion results of direct coherent accumulation without sequence optimization. The proposed algorithm set the initial temperature to 100°C, the annealing coefficient to 0.75, the number of particles in the SA-PSO sequence optimization algorithm to 20, and the number of iterations for the particle swarm optimization (PSO) algorithm and the simulated annealing (SA) algorithm to 10 and 5 times respectively, to ensure the real-time performance of the algorithm. Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 , Figure 12 , Figure 13 , Figure 14 , Figure 15 , Figure 16 The results of echo pulse compression suppression using four different algorithms against different types of ISRJ interference are presented. The LMS algorithm targets the echo from another station for cancellation, such as... Figure 5 , Figure 6, Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 , Figure 12 As shown, the suppression effect is similar to narrow pulse rejection, resulting in a high gate lobe in the processed pulse compression result, indicating poor suppression performance. When dealing with ISRJ-Complex interference, the complexity of the interference makes it impossible for the LMS algorithm to completely suppress it. ACTR is the optimal two-station cancellation result obtained after cancelling between the master station and the other stations.

[0037] The method proposed in this invention, compared to the direct coherent accumulation cancellation signal method, exhibits better suppression performance against ISDJ and ISRJ due to the fusion of multi-station cancellation results. However, against ISRJ-Complex, the lack of adaptive cancellation fusion results results in the accumulation of cancellation results containing high-power noise and residual interference, leading to unstable suppression performance. Furthermore, the target detection distance cells of the ACTR algorithm, the sequence-free optimization method, and the method proposed in this invention are all unbiased and consistent with the actual target location.

[0038] Figure 17 , Figure 18 , Figure 19 The results show that the four algorithms performed 100 Monte Carlo tests against three ISRJ algorithms under JSR conditions of 5dB, 25dB, 30dB, 35dB, and 40dB. The results indicate that the algorithm proposed in this invention outperforms LMS, ACTR, and the no-sequence optimization method in resisting ISDJ, ISRJ, and ISRJ-Complex interference. After interference suppression, the average SJR is 30dB, and the algorithm outperforms ACTR by more than 10dB, demonstrating good robustness under different JSR environments.

[0039] Overall, the algorithm proposed in this invention is stable and performs well in combating these three types of ISRJ. It can not only accumulate target echo energy by utilizing multi-station cancellation results, but also avoid fusing inferior cancellation results through sequence optimization fusion algorithm.

[0040] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. An adaptive distributed radar anti-intermittent sampling and forwarding interference method, characterized in that, Includes the following steps: S1. Perform signal-level interference alignment processing on the echoes from multiple receiving stations, including aligning the interference delay, interference phase, and interference amplitude of the echo signals from multiple receiving stations. S2. The simulated annealing-particle swarm search sequence optimization algorithm is used to realize the intra-pulse coherent level intermittent sampling forwarding interference cancellation fusion processing and optimization under the optimal site combination. This includes coherent interference cancellation between receiving stations, generating candidate fusion signals by prefix sum operation according to the signal-to-noise ratio contribution, evaluating and screening the candidate fusion signals, executing the initialization and iteration process of the sequence optimization simulated annealing-particle swarm search algorithm, and outputting the final fusion signal corresponding to the globally optimal sequence.

2. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 1, characterized in that, The interference alignment process includes using the echo signal from the first receiving station as a reference signal. For the echo signals of the other receiving stations Alignment is then performed.

3. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 1, characterized in that, S1 includes S1.1, interference delay alignment including calculation of reference signal. Compared with the echo signals from other receiving stations The relevant functions are shown in equation (1): （1）； In the formula, The echo signal from the first receiving station. For the conjugate operation of complex numbers, For convolution operations, The value of the echo signal from the m-th receiving station at discrete time -k after conjugation and time reversal. No. The time delay difference between the interference of the receiving station signal and the interference of the reference signal. Depend on The maximum value is obtained as shown in equation (2): （2）； In the formula, Let be the value of the cross-correlation function between the echo signal from the first receiving station and the echo signal from the m-th receiving station at discrete time k. For mold taking operation; when The formula for calculating the signal after interference delay alignment at the m-th receiving station is as follows: As in equation (3): （3）； In the formula, Let be the echo signal from the m-th receiving station, after the first processing, and its value at discrete time n. The echo signal of the m-th receiving station in discrete time The value at; when The formula for calculating the signal after interference delay alignment at the m-th receiving station is as follows: As in equation (4): （4）。 4. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 1, characterized in that, S1 includes S1.2, interference phase alignment dephase difference, as shown in equation (5): （5）； In the formula, Let be the phase difference between the echo signal from the first receiving station and the echo signal from the m-th receiving station. For phase extraction, The value of the echo signal from the first receiving station at discrete time n after the first processing. The value of the echo signal from the m-th receiving station at discrete time -n after the first processing, conjugate operation, and time reversal. Phase compensation is performed based on the phase difference in equation (5), as shown in equation (6): （6）； In the formula, Let be the value of the echo signal from the m-th receiving station at discrete time n after the second processing.

5. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 1, characterized in that, S1 includes S1.3, interference amplitude alignment, which includes utilizing the high-power characteristics of intermittent sampling forwarding interference, in the time domain of the echo signal, locating the interference signal through a dual sliding window bidirectional constant false alarm rate detector, determining the range of the target echo and interference and estimating the amplitude of the interference, and then aligning the interference amplitude of the remaining receiving station signals with the reference signal, as shown in equation (7): （7）； In the formula, Let be the value of the echo signal from the m-th receiving station at discrete time n after the third processing. The interference amplitude of the first receiving station. Let be the interference amplitude of the m-th receiving station.

6. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 1, characterized in that, S2 includes S2.1, interference coherent cancellation between receiving stations, including interference coherent cancellation between the first receiving station and the second receiving station up to the Mth receiving station, the second receiving station and the third receiving station up to the Mth receiving station, ..., the (M-1)th receiving station and the Mth receiving station, and the cancellation result vector. The calculation of elements is as follows, as shown in equation (8): （8）； In the formula, , Let be the value of the echo signal from the i-th receiving station at discrete time n after the third processing. Let be the value of the echo signal from the j-th receiving station at discrete time n after the third processing.

7. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 1, characterized in that, S2 includes S2.2, candidate fusion signal. The calculation, The signal-to-noise ratio contributions of each cancellation result are sorted from largest to smallest, and then... Prefix sum operation generates candidate fusion signals As in equation (9): ； In the formula, , Let be the value of the j-th cancellation result signal at discrete time n.

8. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 1, characterized in that, S2 includes S2.3, evaluation and screening of candidate fusion signals, with the evaluation function as shown in equation (10). （10）； （11）； （12）； In the formula, Candidate fusion signal after pulse compression , To detect the signal-to-noise ratio of the target, for Number of targets after constant false alarm rate detection Whether it is 1, as shown in equation (11), is determined by calculating the 3dB power of the target main lobe. Maximum power at other points The signal-to-noise ratio of the detected target in the candidate fused signal after pulse compression is obtained. , as in equation (12).

9. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 1, characterized in that, S2 includes S2.4, the speed update during the initialization and iteration process of the sequence-optimized simulated annealing-particle swarm search algorithm, and setting the algorithm to initialize the current temperature. P random receiver station sequences are generated as P particles. The initial sequence for the p-th particle is given, and each particle is randomly generated with no more than 3 exchange operators as initial velocities. Then, the algorithm iterates through all particles and performs the following operations, updating the velocity of particle p according to equation (13): （13）； In the formula, For the first The first particle The speed of the next iteration For the first The sequence of particles at time t-1 This is the particle's own historical optimal sequence. This is the historical optimal sequence for all particles. For sequence Transform to sequence speed, and for The probability value within, For probability values Each commutator in the retention action's velocity, For probability values Each commutator in the retention action's velocity, This is to combine all the exchange operators for velocities a and b.

10. The adaptive distributed radar anti-intermittent sampling and forwarding interference method according to claim 9, characterized in that, The iterative process also includes a sequence update and acceptance judgment process for the sequence optimization simulated annealing-particle swarm search algorithm, for particles. The sequence is updated according to formula (14): （14）； In the formula, Let be the sequence of particles at time t. Let be the updated particle velocity at time t. For position Execution speed All commutation operators in the middle; The evaluation value of the particle sequence is assessed using equation (10), and the updated particles are determined according to the Metropolis criterion, as shown in equation (15): （15）； In the formula, for The probability value within, The difference between the new and old sequence evaluation values ​​of the particles is considered. If the result is unacceptable, the particle's velocity and sequence are updated again until the maximum number of iterations is reached. Accepted later; After traversing all particles, update the globally optimal sequence. and the historical optimal sequence of each particle Then proceed to the next iteration, until the maximum number of iterations is reached. Then stop and output the globally optimal sequence. The corresponding final fusion signal .

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