Method and system for designing and optimizing radar smart jamming waveform based on cross-correlation function and genetic algorithm

Abstract

The present application relates to the technical field of radar electronic countermeasure, and particularly relates to a radar intelligent interference waveform design optimization method and system based on cross-correlation function and genetic algorithm, cross-correlation processing is performed on a target echo signal, an interference signal and aliasing signals of the target echo signal, and cross-correlation coefficient characteristic quantities of the aliasing signals and the target echo signal are extracted as a cross-correlation value function; genetic algorithm is used to optimize and design key parameters in waveform design in intermittent sampling and forwarding interference, and radar detection intelligent interference optimization waveforms are generated according to the key parameter optimization results and through maximization of the cross-correlation value function. The present application optimizes and designs non-uniform sampling time, repeated forwarding times and Doppler modulation frequency, which are three core parameters in intermittent sampling and forwarding interference, by using the cross-correlation value function, and designs the above parameters by using a pulse width constraint genetic algorithm, so that the value function is maximized, and optimal design of radar detection intelligent interference waveforms is realized.

Description

Technical Field

[0001] This invention relates to the field of radar electronic countermeasures technology, and in particular to a radar intelligent jamming waveform design optimization method and system based on cross-correlation function and genetic algorithm, for intelligent jamming waveform design for radar detection, so as to achieve effective jamming of radar detection. Background Technology

[0002] The electromagnetic spectrum has become the sixth operational domain in modern warfare, following land, sea, air, space, and cyberspace. Military confrontation in the electromagnetic spectrum is known as electromagnetic spectrum warfare, a higher-level form of combat that has gradually developed from electronic warfare. The concept of cognitive electronic warfare has been proposed, representing a new form of electromagnetic spectrum warfare that endows electromagnetic countermeasure weapons and equipment with autonomous learning and intelligent processing capabilities. Radar countermeasures are a major component of electronic warfare. With the increasing complexity of the electromagnetic environment, the intelligence of adversarial targets, and the development and application of new anti-interception and anti-jamming technologies, radar countermeasure systems must improve their intelligence level. Radars commonly employ the constant false alarm rate (CFAR) method. Effectively jamming radar detection is a key aspect of radar jamming; however, traditional jamming methods generally only use one or a few fixed jamming waveforms, greatly reducing the jamming effect.

[0003] Current research on intelligent jamming waveform design is limited. Existing research focuses on intelligent jamming waveform design for radar based on low probability of intercept (PCI) by utilizing the signal-to-interference-plus-noise ratio (SINR) and mutual information criteria of target detection to minimize the total jamming power. However, this method primarily focuses on low PCI and low power, with insufficient analysis of jamming effects. Other approaches utilize amplitude design of jamming waveforms under constant false alarm rate (CFAR) detection conditions, but these are only applicable to average CFAR detection. Furthermore, reinforcement learning methods such as Q-learning are applied to the parameter optimization design of cognitive jamming waveforms, but this is limited to optimizing only some parameters of the jamming waveform. Summary of the Invention

[0004] To address this, the present invention provides a radar intelligent jamming waveform design optimization method and system based on cross-correlation function and genetic algorithm, which overcomes the limitations of existing jamming waveform parameter optimization design, enables the optimal design of radar detection intelligent jamming waveform, and can effectively jam radar constant false alarm detection.

[0005] According to the design scheme provided by this invention, on the one hand, a radar intelligent jamming waveform design optimization method based on cross-correlation function and genetic algorithm is provided to achieve jamming of target radar detection in radar electronic countermeasures, comprising:

[0006] Cross-correlation processing is performed on the target echo signal, the interference signal and the aliased signal of the target echo signal, and the cross-correlation coefficient feature of the aliased signal and the target echo signal is extracted as the cross-correlation value function.

[0007] A genetic algorithm is used to optimize the key parameters in the waveform design of intermittent sampling forwarding interference. Based on the optimization results of the key parameters, the optimized waveform for radar detection intelligent interference is generated by maximizing the cross-correlation value function. The key parameters include non-uniform sampling time, number of repeated forwardings, and Doppler modulation frequency. In the genetic algorithm, the crossover and mutation directions in the optimal solution search are constrained according to the coupling relationship between the key parameters to ensure that the optimal solution search is carried out in accordance with the direction set by the intermittent sampling forwarding interference.

[0008] As part of the radar intelligent jamming waveform design optimization method based on cross-correlation function and genetic algorithm of this invention, the method further performs cross-correlation processing on the aliased signals of the target echo signal, the jamming signal, and the target echo signal, including:

[0009] First, based on the sampling pulses generated by the jammer within each radar pulse signal time, the sampling signal and the sampling-forwarding interference duration corresponding to the forwarding interference are obtained, and the intermittent sampling pulse signal is obtained based on the sampling pulses and the sampling-forwarding interference duration.

[0010] Next, the intermittent sampling pulse signal is applied to the radar signal to obtain the radar sampling signal. The radar sampling signal is frequency-shifted and modulated by the frequency shift function, and the total forwarding interference signal within a single radar pulse period is obtained by repeated forwarding.

[0011] Then, the radar target echo signal and the aliased signal of the target echo signal and the relay jamming signal are respectively passed through matched filters for pulse compression to obtain the pulse compression output signals corresponding to the radar target echo signal and the aliased signal. The cross-correlation function is then used to perform cross-correlation processing on the pulse compression output signals corresponding to the radar target echo signal and the aliased signal.

[0012] As part of the radar intelligent jamming waveform design optimization method based on cross-correlation function and genetic algorithm of this invention, the method further includes cross-correlation processing of the aliased signals of the target echo signal, the jamming signal, and the target echo signal, and also includes:

[0013] The cross-correlation processing results are subjected to segmented convolution processing to analyze the forwarding interference characteristics of each radar pulse sampling period through the segmented convolution processing process.

[0014] As a radar intelligent jamming waveform design optimization method based on cross-correlation function and genetic algorithm of this invention, the cross-correlation coefficient feature of aliased signal and target echo signal is further extracted as cross-correlation value function, including:

[0015] First, based on the pulse compression output signal of the aliased signal and the pulse compression output signal of the radar target echo signal, the corresponding covariances of the aliased signal and the radar target echo signal are obtained, and the cross-correlation coefficients of the aliased signal and the radar target echo signal are constructed according to the covariances.

[0016] Then, the cross-correlation value function is obtained based on the cross-correlation coefficient.

[0017] As a radar intelligent jamming waveform design optimization method based on cross-correlation function and genetic algorithm of the present invention, the cross-correlation coefficient between aliased signal and radar target echo signal is further expressed as:

[0018] Among them, C tj_t (σ), C tj_tj (σ) and C t_t (σ) represents the covariance of the aliased signal and the radar target echo signal, the autocovariance of the aliased signal, and the autocovariance of the radar target echo signal, respectively.

[0019] As a radar intelligent jamming waveform design optimization method based on cross-correlation function and genetic algorithm of the present invention, the cross-correlation value function is further obtained based on the cross-correlation coefficient, including:

[0020] The reciprocal of the maximum cross-correlation coefficient is selected as the cross-correlation value function between the aliased signal and the radar target echo signal.

[0021] As a radar intelligent jamming waveform design optimization method based on cross-correlation function and genetic algorithm of the present invention, further, the key parameters in waveform design in intermittent sampling and forwarding jamming are optimized using genetic algorithm, including:

[0022] The cross-correlation value function value is used as the fitness value of the genetic algorithm. Through iterative approximation, the genetic algorithm gradually converges to the optimal solution. In the iterative approximation of the genetic algorithm, the crossover and mutation directions are constrained according to the pulse width to ensure that the optimal solution is searched according to the direction set by the intermittent sampling and forwarding interference.

[0023] Furthermore, this invention also provides a radar intelligent jamming waveform design and optimization system based on cross-correlation function and genetic algorithm, used to jam target radar detection in radar electronic countermeasures, comprising: a cross-correlation processing module and an optimization design module, wherein,

[0024] The cross-correlation processing module is used to perform cross-correlation processing on the target echo signal, the interference signal and the aliased signal of the target echo signal, and extract the cross-correlation coefficient feature of the aliased signal and the target echo signal as the cross-correlation value function.

[0025] The optimization design module is used to optimize the key parameters in the waveform design of intermittent sampling forwarding interference using a genetic algorithm. Based on the optimization results of the key parameters, the optimization waveform of radar detection intelligent interference is generated by maximizing the cross-correlation value function. The key parameters include non-uniform sampling time, number of repeated forwardings, and Doppler modulation frequency. In the genetic algorithm, the crossover and mutation directions in the optimal solution search are constrained according to the coupling relationship between the key parameters to ensure that the optimal solution search is carried out in accordance with the direction set by the intermittent sampling forwarding interference.

[0026] The beneficial effects of this invention are:

[0027] This invention, in the cross-correlation value interrupted sampling repeater jamming (CCVISRJ) intelligent jamming waveform design algorithm for intermittent sampling repeater jamming radar detection based on cross-correlation value function and genetic algorithm, draws on the value function concept in reinforcement learning and constructs a cross-correlation value function based on the pulse compression cross-correlation characteristics of the jamming signal and the target echo signal. It optimizes the three core parameters of non-uniform sampling time, number of repeats, and Doppler modulation frequency in intermittent sampling repeater jamming, and uses the pulse width constrained genetic algorithm (PWGA) to optimize the above parameters, thereby maximizing the value function and achieving the optimal design of intelligent jamming waveform for radar detection. Furthermore, through comparative analysis with other interference waveform design methods, the CCVISRJ algorithm of this scheme achieves a smaller cross correlation coefficient (CCC) and is more effective in reducing the correlation of target echo signals. When the interference-to-signal ratio is 10dB, the CCC value can be compressed to below 10%, rendering radar detection completely ineffective. Combined with experimental data analysis, the interference performance of the CCVISRJ algorithm on radar CFAR detection in this scheme can render CA-CFAR detection ineffective and reduce the OS-CFAR detection probability by 5.36%. The effectiveness of this scheme is verified through comparative experiments. Attached image description:

[0028] Figure 1 This is a schematic diagram of the radar intelligent jamming waveform design and optimization process based on cross-correlation function and genetic algorithm in the embodiment;

[0029] Figure 2 This is a schematic diagram of the pulse width constrained genetic algorithm (PWGA) in the embodiment.

[0030] Figure 3 This is a schematic diagram illustrating the interference waveform parameter characteristics analysis when PW=50 and BW=5MHz in the example.

[0031] Figure 4 This is a schematic diagram illustrating the interference waveform parameter characteristics analysis when PW=50 and BW=100MHz in the example.

[0032] Figure 5 This is a schematic diagram of the detection performance of CA-CFAR and OS-CFAR under interference-free conditions in the examples;

[0033] Figure 6 This is a schematic diagram illustrating the comparative analysis of the interference performance of ISRJ, ISPRJ, INUSRJ, and CCVISRJ on CA-CFAR and OS-CFAR detection in the embodiments. Detailed implementation method:

[0034] To make the objectives, technical solutions, and advantages of this invention clearer and more understandable, the invention will be further described in detail below with reference to the accompanying drawings and technical solutions.

[0035] Since interrupted sampling repeater jamming (ISRJ) can be applied to unknown radar signals, the embodiments of this invention are described in detail below. Figure 1 As shown, a radar intelligent jamming waveform design optimization method based on cross-correlation function and genetic algorithm is provided to achieve jamming of target radar detection in radar electronic countermeasures. The method includes the following:

[0036] S101. Perform cross-correlation processing on the target echo signal, the interference signal and the aliased signal of the target echo signal, and extract the cross-correlation coefficient feature of the aliased signal and the target echo signal as the cross-correlation value function.

[0037] Specifically, cross-correlation processing of the aliased signals of the target echo signal, interference signal, and target echo signal can be designed to include the following:

[0038] First, based on the sampling pulses generated by the jammer within each radar pulse signal time, the sampling signal and the sampling-forwarding interference duration corresponding to the forwarding interference are obtained, and the intermittent sampling pulse signal is obtained based on the sampling pulses and the sampling-forwarding interference duration.

[0039] Next, the intermittent sampling pulse signal is applied to the radar signal to obtain the radar sampling signal. The radar sampling signal is frequency-shifted and modulated by the frequency shift function, and the total forwarding interference signal within a single radar pulse period is obtained by repeated forwarding.

[0040] Then, the radar target echo signal and the aliased signal of the target echo signal and the relay jamming signal are respectively passed through matched filters for pulse compression to obtain the pulse compression output signals corresponding to the radar target echo signal and the aliased signal. The cross-correlation function is then used to perform cross-correlation processing on the pulse compression output signals corresponding to the radar target echo signal and the aliased signal.

[0041] The forwarding interference characteristics of each radar pulse sampling period can be analyzed by performing segmented convolution processing on the cross-correlation results.

[0042] The construction of the value function is a crucial step in reinforcement learning algorithms, determining their efficiency and performance. This invention optimizes the radar jamming waveform design using the value function and employs a pulse-width-constrained genetic algorithm to search for waveform parameters that maximize the value function. This allows the jamming signal to better adapt to the characteristics of the radar waveform, achieving effective interference against radar detection. The output waveform of the matched filter is the cross-correlation function of the radar transmitted and received signals. Based on the characteristics of the cross-correlation function, this invention performs cross-correlation processing on the target echo signal, the jamming signal, and the aliased signal of the target echo signal, extracting the cross-correlation coefficient feature as the value function. By optimizing three core parameters, the cross-correlation is reduced. In other words, by adding the jamming signal, the correlation between the target echo signal and the radar transmitted signal is reduced, thereby decreasing radar detection performance.

[0043] Let the time width of the radar transmitted signal be τ. r Then the baseband model of the radar transmitted signal can be expressed as:

[0044]

[0045] For non-uniform sampling forwarding jamming, assuming the jammer generates K sampling pulses within each radar pulse signal time, and the sampling pulse width of the kth sampling pulse is τ. s (k), after frequency shift modulation of the radar signal obtained by sampling the sampling pulse, it is forwarded M(k) times. The modulation function used in the frequency shift modulation is The duration corresponding to the sampled signal and the forwarding interference is T. s (k)=(M(k)+1)·τ s (k). The mathematical expression for the intermittent sampling pulse signal at this time can be expressed as:

[0046]

[0047] The intermittently sampled pulse signal is no longer a regular periodic signal, but a pulse train composed of multiple rectangular pulse signals with different pulse widths and time intervals. Its analysis method is no longer applicable to Fourier series representation.

[0048] Corresponding to the intermittent sampling pulse signal in the above formula, its action on the sampled signal s after the radar signal s (t) can be expressed as:

[0049]

[0050] After obtaining the corresponding radar sampled signal from each sampled pulse in the sampling pulse train, the frequency shift function is used. By performing frequency shift modulation and then repeating the signal M(k) times, the total relay interference signal within a single radar pulse period can be obtained as follows:

[0051]

[0052] Radars generally employ matched filters (pulse compression) to improve their detection performance. The radar transmits a signal of s. r (t), then its matched filter can be set as Interference signal s j The output signal after pulse compression (t) is:

[0053]

[0054] From the above equation, it can be seen that the output waveform after pulse compression of a single sampled and forwarded interference pulse signal is:

[0055]

[0056] Where k is the k-th sampling pulse, and m is the m-th relay of the k-th radar sampling signal. Radar signal s r The pulse pressure result of (t) is:

[0057]

[0058] Comparing the two equations above, it can be seen that the pulse form of a single sampled-forward interference signal is the same as that of a radar signal after pulse compression. The difference lies in the integration interval and the Doppler frequency shift function. Intuitively, this can be understood as the energy or signal amplitude of the pulse-compressed interference signal after a single sample being forwarded being lower than that of the radar pulse-compressed signal, but with a Doppler frequency shift. The following analysis examines the interference effect resulting from Doppler frequency shift modulation after a single sample and repeated forwarding:

[0059]

[0060]

[0061] In the formula, The interference signal is generated by Doppler frequency shift modulation and M(k) repeated relay after sampling the k-th sampling pulse. for The output signal after pulse compression. As can be seen from the above formula, the effect of multiple repeated forwardings is that the frequency-shifted modulated interference pulse signal will be matched and filtered with multiple radar signals of equal duration and then superimposed. The amplitude of the interference signal is higher closer to the center and decreases progressively towards the edges. Since the interference signals repeatedly forwarded within the same forwarding cycle use the same frequency shift function, the frequency shift characteristics after the superposition of multiple interference pulses remain unchanged. It should be noted here that more repetitions do not necessarily mean a larger amplitude. Due to the limitation of radar signal pulse width, the number of repetitions and the interference signal pulse width are inversely proportional. More repetitions result in a narrower interference signal pulse width, which is equivalent to a smaller sampling period for the radar signal, leading to a more refined decomposition of the radar signal.

[0062] For frequency-modulated radar signals, the frequency modulation signal interference characteristics of the CCVISRJ algorithm implemented in this case are analyzed as follows:

[0063] The most classic linear frequency modulation (LFM) radar signal is used for analysis. The output waveform of the interference signal after pulse compression is derived, and the influence of parameters such as non-uniform sampling time, number of repeated forwards, and Doppler modulation frequency on the interference waveform is analyzed.

[0064] Assume the time width of the radar transmitted signal is τ. r If the bandwidth is B, then the baseband model of the radar transmitted signal can be expressed as:

[0065]

[0066] In the formula, μ=B / τ r The frequency modulation slope. After obtaining the corresponding radar sampled signal from each sampled pulse in the sampling pulse train, the frequency shift function is used. By performing frequency shift modulation and then repeating the signal M(k) times, the total relay interference signal within a single radar pulse period can be obtained as follows:

[0067]

[0068] The output signal of the interference signal after the m-th forward of the k-th sampled signal is processed by radar matched filtering is:

[0069]

[0070] From the above formula, it can be seen that the main lobe center of the output signal after radar matched filtering of the interference signal of the k-th sampled signal in the m-th relay is... Amplitude τ s (k), the main lobe width is 2 / (μτ) s(k)). Side lobe center position The conditions to be met are The amplitude of the side lobe center is The side lobe width is 1 / (μτ) s (k)).

[0071] based on The signal obtained by repeating the interference signal M(k) times after the k-th sampled signal is received, and the output signal after radar matched filtering, can be expressed as:

[0072]

[0073] From the above equation, it can be seen that τ s (k) mainly affects the amplitude and peak position of the interference signal. τ s A larger value for (k) results in a larger amplitude of the interference signal, because τ s The larger (k) is, the greater the energy of the radar signal obtained from sampling, and naturally the greater the energy of the interference signal after retransmission and modulation. However, τ s A larger M(k) results in a greater delay in the main lobe of the interference signal, meaning it lags further behind the target location. M(k) primarily affects the time-domain coverage of the interference signal; a larger M(k) indicates a greater number of interference signals and a wider time-domain coverage. It is an important modulation parameter for deceptive and suppression interference. It mainly affects the phase of the interference signal.

[0074] For phase-modulated radar signals, the frequency modulation signal interference characteristics of the CCVISRJ algorithm implemented in this embodiment are analyzed as follows:

[0075] Pseudo-random sequences are commonly used phase-modulated signals, and their signal expression is as follows:

[0076]

[0077] In the formula, τ rc Let τ be the symbol width and P be the pseudo-random sequence length, then the pulse width of the radar pulse is τ. r =P·τ rc c m For a binary phase pseudo-random sequence coded signal, c is the symbol value. m The value of is only ±1. For a binary pseudo-random sequence coded signal, the output signal of the interference signal after radar matched filtering of the m-th forward of the k-th sampled signal is:

[0078]

[0079] Where χ(τ,ξ) is the ambiguity function of the radar transmitted signal. fsk =1 / [(M(k)+1)·τ s (k)].

[0080] based on The signal obtained by repeating the interference signal M(k) times after the k-th sampled signal is received, and the output signal after radar matched filtering, can be expressed as:

[0081]

[0082] As can be seen from the above formula, the output of the interference signal after non-uniform intermittent sampling and forwarding of the binary pseudo-random sequence encoded signal and radar matched filtering is the time-delayed superposition of the sum of equally spaced false targets and the dense false targets surrounding it. Here, equally spaced false targets are generated by intermittent sampling, dense false targets are generated by pseudo-random sequence phase modulation, and the time-delay superposition is determined by repeated forwarding.

[0083] Let the radar target echo signal be s. t (t), then the output waveforms of the target echo signal and the aliased signal of the target echo signal and the interference signal after matched filtering are:

[0084]

[0085]

[0086] s tj_mf (t) and s t_mf The cross-correlation function of (t) is:

[0087]

[0088] As can be seen from the above formula, R can be... tj_t (σ) Perform segmented convolution processing to analyze the forwarding interference characteristics of each sampling period separately:

[0089]

[0090] in, It is the signal after matched filtering of the aliased signal of the m-th interference pulse signal and the radar echo signal within the k-th sampling and forwarding interference period, and then compared with s. t_mf * The cross-correlation function of (t). Note that the value of m differs from that of s. j (t) signal, where m can take the value 0. When m = 0, it is the sampling time for intermittent sampling and forwarding interference, and s at this time tj The (t) signal contains only the target echo signal.

[0091] Among them, the cross-correlation coefficient feature of the aliased signal and the target echo signal is extracted as the cross-correlation value function, which can be designed to include:

[0092] First, based on the pulse compression output signal of the aliased signal and the pulse compression output signal of the radar target echo signal, the corresponding covariances of the aliased signal and the radar target echo signal are obtained, and the cross-correlation coefficients of the aliased signal and the radar target echo signal are constructed according to the covariances.

[0093] Then, the cross-correlation value function is obtained based on the cross-correlation coefficient.

[0094] Similar to the cross-correlation function, s can be defined. tj_mf (t) and s t_mf The covariance function of (t) is:

[0095] C tj_t (σ)=E{[s tj_mf (t)-μ tj_mf (t)][s t_mf (t-σ)-μ t_mf ] *}

[0096] =R tj_t (σ)-μ tj_mf (t)·μ t_mf *

[0097] Similarly, C can be calculated. tj_tj (σ) and C t_t (σ), then the cross-correlation coefficient between the aliased signal and the pulse-compressed echo signal can be expressed as:

[0098]

[0099] In this embodiment, the maximum value ρ of the cross-correlation coefficient can be selected. tj_t The reciprocal of (0) can be used as the cross-correlation value function, which can be expressed as:

[0100]

[0101] From the expression of the cross-correlation value function, it can be seen that V cc The larger the value, the smaller the cross-correlation between the aliased signal and the echo signal after pulse compression, and the better the interference effect. Therefore, the interference waveform design goal in this scheme is to pursue V. cc Maximize the value.

[0102] S102. The key parameters in the waveform design of intermittent sampling forwarding interference are optimized using a genetic algorithm. Based on the optimization results of the key parameters, the radar detection intelligent interference optimization waveform is generated by maximizing the cross-correlation value function. The key parameters include non-uniform sampling time, number of repeated forwardings, and Doppler modulation frequency. In the genetic algorithm, the crossover and mutation directions in the optimal solution search are constrained according to the coupling relationship between the key parameters to ensure that the optimal solution search is carried out in accordance with the direction set by the intermittent sampling forwarding interference.

[0103] Among them, the key parameters in the waveform design for intermittent sampling and forwarding interference are optimized using a genetic algorithm, and can be designed to include:

[0104] The cross-correlation value function value is used as the fitness value of the genetic algorithm. Through iterative approximation, the genetic algorithm gradually converges to the optimal solution. In the iterative approximation of the genetic algorithm, the crossover and mutation directions are constrained according to the pulse width to ensure that the optimal solution is searched according to the direction set by the intermittent sampling and forwarding interference.

[0105] Optimal parameter design can be achieved using a Genetic Algorithm (GA) to search for the optimal solution. GA searches by simulating a natural evolutionary process, gradually converging to the optimal solution through iterative approximation. In this embodiment, the GA is improved based on the coupling relationship between the three core parameters: non-uniform sampling time, number of repeated forwards, and Doppler modulation frequency. It no longer simply defines a search area and performs an indiscriminate uniform search. Based on the characteristics of radar jamming signals, the crossover and mutation steps in the GA are improved, such as... Figure 2 As shown, the PWGA algorithm is proposed. The following relationship exists between the intermittent sampling and forwarding jamming signal and the radar transmitted signal:

[0106]

[0107] Ideally, the pulse width of the jamming signal should be equal to the pulse width of the radar signal.

[0108]

[0109] Under the above constraints, the interference signal can be made to enter the matched filter of the radar receiver as much as possible, increasing the energy of the interference signal. At the same time, the length of the interference signal is the same as that of the radar signal, avoiding truncation by the radar's anti-jamming technology and affecting the jamming effect. PWGA can effectively constrain the crossover and mutation directions, avoiding overly broad disordered inheritance, which will be more conducive to the progress of search iteration and ensure that the optimal solution search is carried out in the set direction that satisfies the intermittent sampling forwarding interference.

[0110] Figure 2The fitness value in the algorithm is calculated using the cross-correlation function value V. cc When performing crossover and variation, the genetic direction is constrained according to the pulse width to avoid broad and disordered inheritance, improve genetic efficiency, and meet the parameter optimization search in intelligent interference waveform design.

[0111] Furthermore, based on the above method, this embodiment of the invention also provides a radar intelligent jamming waveform design and optimization system based on cross-correlation function and genetic algorithm, used to jam target radar detection in radar electronic countermeasures, comprising: a cross-correlation processing module and an optimization design module, wherein,

[0112] The cross-correlation processing module is used to perform cross-correlation processing on the target echo signal, the interference signal and the aliased signal of the target echo signal, and extract the cross-correlation coefficient feature of the aliased signal and the target echo signal as the cross-correlation value function.

[0113] The optimization design module is used to optimize the key parameters in the waveform design of intermittent sampling forwarding interference using a genetic algorithm. Based on the optimization results of the key parameters, the optimization waveform of radar detection intelligent interference is generated by maximizing the cross-correlation value function. The key parameters include non-uniform sampling time, number of repeated forwardings, and Doppler modulation frequency. In the genetic algorithm, the crossover and mutation directions in the optimal solution search are constrained according to the coupling relationship between the key parameters to ensure that the optimal solution search is carried out in accordance with the direction set by the intermittent sampling forwarding interference.

[0114] To verify the effectiveness of this solution, the following explanation is provided based on experimental data:

[0115] The above analysis shows that the CCVISRJ algorithm in this embodiment includes three core parameters: non-uniform sampling time τ. s (k), number of repeated forwardings M(k), Doppler modulation frequency The controlled variable method is used in the simulation to analyze τ. s (k), M(k) and Impact on the characteristics of interference waveforms.

[0116] Figure 3 It is in τ s (k)=2μs, M(k)=2, Under the condition, change the parameter τ respectively s (k), M(k), Interference signal after Comparison of output signals after matched filtering. From (a), it can be seen that when M(k) = 2, the interference signal pulse compression will have two peaks, and τ s The peak amplitude under the condition (k) = 8μs is τ s Four times that of (k) = 2μs, and The derivation formula is consistent with the conclusion that the amplitude value of the interference signal pulse compression is equal to τ. s (k) shows a linear relationship. τ s The peak positions under the condition (k) = 8 μs are 58 μs and 66 μs, respectively, which is also consistent with... The theoretical analysis results are consistent. As can be seen from (b), when M(k) = 8, the peak values ​​of the output waveform after pulse compression of the interference signal also correspondingly become 8, which is consistent with... The theoretical derivation results are consistent. It can be seen from (c) that when… When the interference signal is pulse-compressed, a significant phase jump will occur in the signal, which is very effective in interfering with radar pulse accumulation processing. However, there is a noticeable phenomenon in the figure: when τ s (k)=2μs, M(k)=2, In (a), the peak position of the interference signal after pulse compression differs significantly from the theoretical derivation, and its amplitude value is not completely equal. In (b), although there are 8 signal peaks, the amplitude difference is large, and the distribution position is visually quite different from the theoretical result. In particular, at the first peak in the dashed rectangle, there are many side lobes and the peak value is high.

[0117] All of the above problems can be solved in The answer can be found in the formula; the widths of the main lobe and side lobes are determined by two parameters: μ and τ. s (k), due to the bandwidth BW = 5MHz and τ used in the simulation s (k) = 2μs, which makes the widths of both the main lobe and side lobes very large, causing them to overlap. The resulting peak position differs from the main lobe peak position. This can be seen from (a) when τ s (k): From 2μs to 8μs, the widths of the main lobe and side lobes narrow, the influence of the side lobes on the shape of the main lobe decreases, and the peak position after superposition is consistent with the peak position of the main lobe, satisfying the theoretical analysis results. In order to more intuitively verify the theoretical formula of the interference signal waveform and analyze the variation law of the CCVISRJ interference waveform parameter characteristics, the following simulation analysis is performed with BW=100MHz.

[0118] from Figure 4 As can be seen, when BW = 100MHz, due to the sufficiently large bandwidth (i.e., a sufficiently large value), both the main lobe and side lobe widths are small, and the amplitude values ​​of the side lobes are also very small. Therefore, the side lobes have very little influence on the deformation of the main lobe. The simulation results and the derived values... The theoretical analysis results for the main lobe center position, amplitude, and number of peaks in the expression are consistent, which fully demonstrates the correctness of the mathematical expression for the interference waveform. It can be used for radar intelligent interference waveform design and more targeted intelligent interference waveform design can be carried out based on the influence characteristics of each parameter on the interference waveform.

[0119] Interrupted sampling repeater jamming (ISRJ) has both deception and suppression effects; whether it reduces the detection rate or increases the false alarm rate, it all boils down to a decrease in radar detection performance. This paper compares and analyzes the traditional ISRJ algorithm, the ISPRJ algorithm, and the tabu search-based non-uniform sampling repeater jamming (INUSRJ) algorithm to verify the effectiveness of the CCVISRJ algorithm. Radar constant false alarm rate (CFAR) algorithms include mean-based CFAR and statistical CFAR. The most typical mean-based CFAR is CA-CFAR, and the most typical statistical CFAR is OS-CFAR. To quantitatively analyze the jamming characteristics, this invention uses CA-CFAR and OS-CFAR to simulate and analyze the performance of the radar intelligent jamming waveform design method based on the CCVISRJ algorithm. In the simulation experiment, the false alarm rate was set to 10. -6 The number of left and right reference units is N each. ref =10, the protection unit is set to 3, and k in the OS-CFAR algorithm OS =15, the target is located within the 250th range cell, and the signal-to-noise ratio is set to 10dB.

[0120] The CA-CFAR algorithm utilizes N on the left and right. ref The detection threshold is calculated by taking the average of the sampled values ​​from each reference cell. And obtain the corresponding detection probability.

[0121]

[0122] In the formula, The reference cell sample value, The average signal-to-noise ratio. The OS-CFAR detection algorithm first sorts all sample values ​​within the left and right reference cells from smallest to largest, typically selecting the k-th value. OSThe minimum value is used as the background clutter noise power estimate. The detection probability expression of the OS-CFAR algorithm is:

[0123]

[0124] In the formula, Γ(x) is the gamma function.

[0125] Figure 5 The results of CFAR detection of radar target echoes under interference-free conditions are presented. Both CA-CFAR and OS-CFAR detection methods were used, and both achieved effective target detection. The detection probability of CA-CFAR was 99.52%, and that of OS-CFAR was 99.99%, with no false targets detected. This demonstrates that under these simulation conditions, the radar can effectively detect targets using CFAR without interference signals.

[0126] Figure 6The results of radar CFAR detection of targets under four different interference conditions are presented. As can be seen from the figures, the radar can achieve CFAR detection of targets under ISRJ, ISPRJ, and INUSRJ interference conditions. In particular, the detection probability of the OS-CFAR method is around 99%, and even under CCVISRJ interference, the detection probability of OS-CFAR reaches approximately 94.64%, a decrease of 5.36%. However, the detection probability of CA-CFAR is very low under all four interference conditions, especially under CCVISRJ interference, where detection is completely ineffective. These results are closely related to the detection characteristics of the CA-CFAR and OS-CFAR methods. CA-CFAR performs best in a uniform background environment, but its performance drops sharply once interference signals are present. OS-CFAR has excellent detection performance in multi-target backgrounds; intermittent sampling and forwarding interference can be understood as multiple artificially created false targets, so its interference effect on OS-CFAR detection performance is relatively small. As can be seen from the figure, the CCVISRJ jamming method exhibits superior jamming performance compared to the ISRJ, ISPRJ, and INUSRJ methods, regardless of whether the detection method is CA-CFAR or OS-CFAR. There are two main reasons for this: First, CCVISRJ fully utilizes the phase characteristics of the relay jamming signal, generating an anti-phase jamming signal at the target location. This signal, after aliasing with the target echo signal, effectively reduces the amplitude of the target echo. For example, in the CCVISRJ jamming effect figure, the target power is 73.36 dB, a decrease of 4.21 dB compared to INUSRJ, which only considers the sampling pulse width and the number of relays. Secondly, the CCVISRJ jamming method can better distribute the jamming signal energy in front of and behind the target signal, unlike the ISPRJ and INUSRJ methods which mainly set the jamming energy at the far end of the target. For example, in the CCVISRJ jamming effect diagram, the highest peak value in front of and behind the target is 5.55dB higher than that of the INUSRJ method. This characteristic allows the CCVISRJ jamming method to set more jamming signal energy within the CFAR reference cell, and the distribution is irregular, making it difficult for radar anti-jamming methods to effectively filter it out.

[0127] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0128] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0129] The units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations are not considered to be beyond the scope of this invention.

[0130] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiments can be implemented in hardware or as a software functional module. This invention is not limited to any particular combination of hardware and software.

[0131] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A radar intelligent jamming waveform design and optimization method based on cross-correlation function and genetic algorithm, used to jam target radar detection in radar electronic countermeasures, characterized in that, Include: Based on the sampling pulses generated by the jammer within each radar pulse signal time, the sampling signal and the corresponding sampling-forwarding interference duration are obtained. Intermittent sampling pulse signals are then acquired based on the sampling pulses and the sampling-forwarding interference duration. These intermittent sampling pulse signals are applied to the radar signal to obtain the radar sampling signal. The radar sampling signal is frequency-shifted using a frequency-shift function, and the total forwarding interference signal within a single radar pulse period is obtained through repeated forwarding. The radar target echo signal and the aliased signal between the target echo signal and the forwarding interference signal are pulse-compressed through matched filters to obtain the pulse-compressed output signals corresponding to the radar target echo signal and the aliased signal. A cross-correlation function is used to cross-correlate the pulse-compressed output signals corresponding to both the radar target echo signal and the aliased signal, and the cross-correlation coefficient characteristic of the aliased signal and the target echo signal is extracted as the cross-correlation value function. A genetic algorithm is used to optimize the key parameters in the waveform design of intermittent sampling forwarding interference. Based on the optimization results of the key parameters, the optimized waveform for radar detection intelligent interference is generated by maximizing the cross-correlation value function. The key parameters include non-uniform sampling time, number of repeated forwardings, and Doppler modulation frequency. In the genetic algorithm, the crossover and mutation directions in the optimal solution search are constrained according to the coupling relationship between the key parameters to ensure that the optimal solution search is carried out in accordance with the direction set by the intermittent sampling forwarding interference.

2. The radar intelligent jamming waveform design and optimization method based on cross-correlation function and genetic algorithm according to claim 1, characterized in that, The cross-correlation processing of the target echo signal, the interference signal, and the aliased signal of the target echo signal also includes: The cross-correlation processing results are subjected to segmented convolution processing to analyze the forwarding interference characteristics of each radar pulse sampling period through the segmented convolution processing process.

3. The radar intelligent jamming waveform design and optimization method based on cross-correlation function and genetic algorithm according to claim 1, characterized in that, The cross-correlation coefficient feature between the aliased signal and the target echo signal is extracted as the cross-correlation value function, which includes: First, based on the pulse compression output signal of the aliased signal and the pulse compression output signal of the radar target echo signal, the corresponding covariances of the aliased signal and the radar target echo signal are obtained, and the cross-correlation coefficients of the aliased signal and the radar target echo signal are constructed according to the covariances. Then, the cross-correlation value function is obtained based on the cross-correlation coefficient.

4. The radar intelligent jamming waveform design and optimization method based on cross-correlation function and genetic algorithm according to claim 3, characterized in that, The cross-correlation coefficient between the aliased signal and the radar target echo signal is expressed as: ,in, , and These are the covariances of the aliased signal and the radar target echo signal, the autocovariance of the aliased signal, and the autocovariance of the radar target echo signal, respectively.

5. The radar intelligent jamming waveform design and optimization method based on cross-correlation function and genetic algorithm according to claim 4, characterized in that, The cross-correlation value function, derived from the cross-correlation coefficient, includes: The reciprocal of the maximum cross-correlation coefficient is selected as the cross-correlation value function between the aliased signal and the radar target echo signal.

6. The radar intelligent jamming waveform design and optimization method based on cross-correlation function and genetic algorithm according to claim 1, characterized in that, The genetic algorithm is used to optimize the key parameters in waveform design for intermittent sampling and forwarding interference, including: The cross-correlation value function value is used as the fitness value of the genetic algorithm. Through iterative approximation, the genetic algorithm gradually converges to the optimal solution. In the iterative approximation of the genetic algorithm, the crossover and mutation directions are constrained according to the pulse width to ensure that the optimal solution is searched according to the direction set by the intermittent sampling and forwarding interference.

7. A radar intelligent jamming waveform design and optimization system based on cross-correlation function and genetic algorithm, used to jam target radar detection in radar electronic countermeasures, characterized in that, It includes: a cross-correlation processing module and an optimization design module, wherein, The cross-correlation processing module is used to obtain the sampling signal and the corresponding sampling-forwarding interference duration based on the sampling pulses generated by the jammer within each radar pulse signal time. It also obtains intermittent sampling pulse signals based on the sampling pulses and the sampling-forwarding interference duration, applies the intermittent sampling pulse signals to the radar signal to obtain the radar sampling signal, performs frequency shift modulation on the radar sampling signal through a frequency shift function, and obtains the total forwarding interference signal within a single radar pulse period through repeated forwarding. The radar target echo signal and the aliased signal of the target echo signal and the forwarding interference signal are respectively processed by a matched filter for pulse compression to obtain the pulse compression output signals corresponding to the radar target echo signal and the aliased signal. The cross-correlation function is used to perform cross-correlation processing on the pulse compression output signals corresponding to the radar target echo signal and the aliased signal, and the cross-correlation coefficient feature of the aliased signal and the target echo signal is extracted as the cross-correlation value function. The optimization design module is used to optimize the key parameters in the waveform design of intermittent sampling forwarding interference using a genetic algorithm. Based on the optimization results of the key parameters, the optimization waveform of radar detection intelligent interference is generated by maximizing the cross-correlation value function. The key parameters include non-uniform sampling time, number of repeated forwardings, and Doppler modulation frequency. In the genetic algorithm, the crossover and mutation directions in the optimal solution search are constrained according to the coupling relationship between the key parameters to ensure that the optimal solution search is carried out in accordance with the direction set by the intermittent sampling forwarding interference.

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