ISRJ suppression method based on multi-model fusion

Abstract

The invention provides an ISRJ suppression method based on multi-model fusion, and relates to the technical field of ISRJ suppression, and the method specifically comprises the following steps: constructing an ISRJ signal model; and extracting interference fragments by using a fusion interference detection method based on Transform and a decision tree. On the basis of the extracted interference fragments, complex variational mode decomposition (CVMD) is adopted to extract continuous components of the ISRJ, and the continuous components of the ISRJ are decomposed into band-limited intrinsic mode functions BIMFs; and selecting the interference signal and the target signal based on the estimated interference signal-to-noise ratio, and reconstructing the selected interference signal. And reconstructing the interference signal by using multi-scale wavelet decomposition. And fusing the reconstructed interference signals. And reconstructing a target signal and an interference signal by adopting an alternating iteration method. According to the technical scheme, the problems that complex modulation signals cannot be accurately represented and target signals cannot be completely reserved in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of ISRJ inhibition, and in particular to an ISRJ inhibition method based on multi-model fusion. Background Art

[0002] Intermittent sampling repeater jammer (ISRJ) is a typical active mainlobe jammer with high power, strong coherence and complex modulation characteristics. It generates a large number of false signal peaks, which mask the real target signal and lead to wrong detection results. Therefore, it is very important to develop a robust mainlobe ISRJ suppression method, and many researchers have been studying this problem in recent years.

[0003] ISRJ suppression methods can be mainly divided into three categories: interference signal filtering, target signal extraction, and interference reconstruction. In the interference signal filtering method, the interference signal is represented parameterized or semi-parametrically, modeled in the form of known signals (such as chirp or cosine signals), and a time-frequency filter is designed to remove the interference. Although this method is computationally efficient, the residual interference is more significant due to the mismatch between the complex modulation of the interference and the assumed signal form. In the target signal extraction method, the interference position is first detected and set to zero, and then the target signal is extracted from the uninterrupted area using sparse recovery techniques. However, this method inevitably leads to signal loss and may miss detection under low signal-to-noise ratio (SNR) and high-density interference conditions. In the interference reconstruction method, the main signal components are extracted from multiple consistent interference fragments using techniques such as principal component analysis (PCA) or dictionary learning. These methods have high reconstruction accuracy for stable or slowly changing ISRJs, but their performance will drop significantly when the interference changes rapidly.

[0004] Therefore, there is a need for an ISRJ suppression method based on multi-model fusion that can accurately characterize complex modulation signals and completely preserve target signals. Summary of the invention

[0005] The main purpose of the present invention is to provide an ISRJ suppression method based on multi-model fusion to solve the problem that the prior art cannot accurately characterize complex modulation signals and completely retain target signals.

[0006] To achieve the above object, the present invention provides an ISRJ inhibition method based on multi-model fusion, which specifically comprises the following steps: S1, build ISRJ signal model.

[0007] S2, extracts interference fragments using a fusion interference detection method based on Transformer and decision tree.

[0008] S3, based on the extracted interference fragments, complex variational mode decomposition (CVMD) is used to extract the continuous components of ISRJ, and the continuous components of ISRJ are decomposed into band-limited intrinsic mode functions (BIMFs).

[0009] S4, based on the estimated interference signal-to-noise ratio, selecting an interference signal and a target signal, and reconstructing the selected interference signal.

[0010] S5, reconstruct the interference signal using multi-scale wavelet decomposition.

[0011] S6, fusing the interference signals reconstructed in steps S4 and S5.

[0012] S7, using the interference signal obtained in step S6 to eliminate the interference of the original signal, reconstructing the target signal using a sparse reconstruction algorithm, and then using the reconstructed target signal to eliminate the target signal in the original signal and reconstruct the interference signal, and repeating this process alternately to reconstruct the interference signal and the target signal.

[0013] Furthermore, step S1 specifically includes the following steps: S1.1, assuming that the radar transmit waveform is , is modeled as a linear frequency modulation LFM signal; The ISRJ signal composed of fragments is expressed as : ; in, is the interference amplitude after power amplification, is a rectangular pulse function, For the The storage time of the interference fragments, is the length of the interference fragment.

[0014] S1.2, modulate each interference segment, and the modulated ISRJ signal is expressed as : ; in, is the convolution operator, is the random modulation kernel.

[0015] S1.3, if there is targets, the total received signal is expressed as : ; in, For the The range of the target, For the The time delay of the target echo is is Gaussian white noise; after sampling, Represented as a vector .

[0016] Furthermore, step S3 specifically includes the following steps: S3.1, Band-limited intrinsic mode functions BIMFs: ; ; in, is the center frequency, is the modal function, The decomposed BIMFs, is the number of BIMFs, is the target signal or the observed signal, is an exponential function, is the minimum value function.

[0017] S3.2, the band-limited intrinsic mode functions BIMFs are solved using the augmented Lagrangian method with alternating direction multiplier method ADMM.

[0018] Furthermore, step S4 specifically includes the following steps: S4.1, Interference signal reconstructed using selected BIMFs It is expressed as: ; in, is the support set of the selected BIMFs, estimated as: ; in, is the number of selected BIMFs components, is the signal length, are the power of the signal and the noise, is the threshold factor, for The square of the 2-norm of is the parameter minimum function.

[0019] S4.2, using greedy algorithm to solve , the reconstructed interference signal The discrete digital signal is represented as a vector .

[0020] Furthermore, step S5 specifically includes the following steps: S5.1, using the interference fragment obtained in step S2, the interference wavelet coefficients of different expansion and translation factors are expressed as : ; in, is the complex conjugate of the mother wavelet, Is a dilation factor and translation factor The wavelet kernel is expressed as: ; in, For the mother wavelet.

[0021] S5.2, design an adaptive threshold to separate the pulse interference component and the target signal component as follows: ; ;

[0022] in, is the threshold factor associated with JSRN, is the adjustment factor, is the processed wavelet coefficient, is a nonlinear function, is a sign function.

[0023] S5.3, the pulse component extracted by wavelet is expressed as : ; The discrete digital signal is represented as a vector .

[0024] Furthermore, step S6 specifically includes the following steps: S6.1, the following weighted fusion filter is proposed : ; in, , is the weight.

[0025] S6.2, order , using the criterion of minimizing the residual square error to determine the variables to be solved , as shown below: ; in, Express Taking the conjugate transpose, for The optimal solution of

[0026] Through convex optimization, we get: ; ; ; in, and is the intermediate variable, is the conjugate operation, for The transpose of .

[0027] S6.3, by Interference cancellation is performed and the target signal is extracted by combining the sparse reconstruction method. , ; in, is a sparse dictionary associated with the transmission waveform, is the penalty factor, To solve the target signal The optimal solution of .

[0028] S6.4, solved by the conjugate gradient method .

[0029] The present invention has the following beneficial effects: The present invention analyzes interference characteristics based on a multi-domain decomposition and reconstruction method, can capture complex signal structures, and achieve accurate interference signal estimation. The present invention uses the estimated interference signal to offset the interference in the echo, thereby completely retaining the target signal structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the specific implementation or the prior art description. Obviously, the drawings described below are some implementations of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A flow chart of an ISRJ inhibition method based on multi-model fusion of the present invention is shown.

[0031] Figure 2 A simulation diagram of a typical ISRJ in the time domain is shown.

[0032] Figure 3 The time-frequency distribution TFD diagram of interference is shown.

[0033] Figure 4 A simulated echogram in the time domain is shown.

[0034] Figure 5 A simulated echogram in a TFD is shown.

[0035] Figure 6 The TFD diagram of the method provided by the present invention is shown.

[0036] Figure 7 A TFD diagram using CVMD is shown.

[0037] Figure 8 The TFD plot using wavelet is shown.

[0038] Fig. 9 The resulting graph after pulse compression is shown.

[0039] Fig.10 A comparison of signal power loss of different methods is shown.

[0040] Fig.11 A comparison of residual JSNR of different methods is shown.

[0041] Fig.12 A comparison chart of detection probabilities of different methods is shown. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] like Figure 1 The ISRJ suppression method based on multi-model fusion shown in the figure specifically includes the following steps: S1, build ISRJ signal model.

[0044] S2, extracts interference fragments using a fusion interference detection method based on Transformer and decision tree.

[0045] S3, based on the extracted interference fragments, complex variational mode decomposition (CVMD) is used to extract the continuous components of ISRJ, and the continuous components of ISRJ are decomposed into band-limited intrinsic mode functions (BIMFs).

[0046] S4, based on the estimated interference signal-to-noise ratio, selecting an interference signal and a target signal, and reconstructing the selected interference signal.

[0047] S5, reconstruct the interference signal using multi-scale wavelet decomposition.

[0048] S6, fusing the interference signals reconstructed in steps S4 and S5.

[0049] S7, use the interference signal obtained in step S6 to eliminate the interference of the original signal, reconstruct the target signal using a sparse reconstruction algorithm, and then use the reconstructed target signal to eliminate the target signal in the original signal and reconstruct the interference signal, and repeat this process alternately, that is, loop this process to alternately reconstruct the interference signal and the target signal, so as to improve the accuracy of the interference signal reconstruction.

[0050] Specifically, step S1 includes the following steps: S1.1, the jammer intercepts the radar signal and uses the "store-and-forward" method to send intermittent sampling and forwarding jamming (ISRJ) through multiple signal segments. Assume that the radar transmit waveform is , is modeled as a linear frequency modulation LFM signal; The ISRJ signal composed of fragments is expressed as : ; in, is the interference amplitude after power amplification, is a rectangular pulse function, For the The storage time of the interference fragments, is the length of the interference fragment.

[0051] S1.2, after modulation, the ISRJ signal is expressed as : ; in, is the convolution operator, is the random modulation kernel.

[0052] S1.3, if there is targets, the total received signal is expressed as : ; in, For the The range of the target, For the The time delay of the target echo is is Gaussian white noise; after sampling, Represented as a vector .

[0053] Based on the signal model: , a typical ISRJ is simulated in the time domain, such as Figure 2As shown in Figure 1. It can be observed that the ISRJ has strong coherence with the radar signal, complex modulation characteristics and high power, which enables it to effectively cover up the real target signal and cause detection failure. In the time-frequency distribution (TFD) of the interference, as shown in Figure 1. Figure 3 As shown in Figure 1, the modulation characteristics of continuous interference and pulse interference are mixed, which increases the complexity of interference analysis. In this context, accurately characterizing complex modulation characteristics is the key to achieving effective interference suppression.

[0054] Specifically, step S2 utilizes <Intensive Interrupted Sampling Repeater JammingDetection Based on Transformer CFAR Fusion Detection Model> The method in (Transformation-based Dense Interruption Sampling Repeater Interference Detection) accurately locates the interference fragments.

[0055] Specifically, based on the extracted interference fragments, complex variational mode decomposition (CVMD) is used to extract the continuous components of ISRJ. CVMD regards the extracted interference as a band-limited signal with specific sparsity and decomposes it into band-limited intrinsic mode functions (BIMFs). BIMFs have the characteristics of smooth continuity and sparsity because band-limited Gaussian smoothing and time derivative minimization are introduced in the decomposition process. Step S3 specifically includes the following steps:

[0056] S3.1, Band-limited intrinsic mode functions BIMFs: ; ; in, is the center frequency, is the modal function, The decomposed BIMFs, is the number of BIMFs, is the target signal or the observed signal, is an exponential function, is the minimum value function.

[0057] S3.2, the band-limited intrinsic mode functions BIMFs are solved using the augmented Lagrangian method with alternating direction multiplier method ADMM.

[0058] Since the jammer and the target signal are always mixed together, if all BIMFs are used for jammer reconstruction, the target signal will be lost. To solve this problem, a BIMFs selection criterion is designed based on the jammer signal-to-noise ratio estimated in "Intensive Interrupted Sampling Repeater Jamming Detection Based on Transformer CFAR Fusion Detection Model".

[0059] Specifically, step S4 includes the following steps: S4.1, Interference signal reconstructed using selected BIMFs It is expressed as: ; in, is the support set of the selected BIMFs, estimated as: ; in, is the number of selected BIMFs components, is the signal length, is the power of the signal and the noise, which are estimated simultaneously during the interference localization process. is the threshold factor, usually set between 1.5 and 2. for The square of the 2-norm of corresponds to the energy of the BIMFs component, is the parameter minimum function.

[0060] S4.2, using greedy algorithm to solve , the reconstructed interference signal The discrete digital signal is represented as a vector .

[0061] In order to improve the accuracy of reconstructing interference, multi-scale wavelet decomposition is also used to extract the characteristics of the pulse signal. This step uses the wavelet kernel after expansion and translation to decompose the signal at different scales.

[0062] Specifically, step S5 includes the following steps: S5.1, using the interference fragment obtained in step S2, the interference wavelet coefficients of different expansion and translation factors are expressed as : ; in, is the complex conjugate of the mother wavelet, Is a dilation factor and translation factor The wavelet kernel is expressed as: ; in, For the mother wavelet.

[0063] S5.2, design an adaptive threshold to separate the pulse interference component and the target signal component as follows: ; ; in, is the threshold factor associated with JSRN, is the adjustment factor, is the processed wavelet coefficient, It is a nonlinear function used to adjust the wavelet coefficients greater than the threshold λ. It is a sign function, which is used to restore the sign direction of the original wavelet coefficients.

[0064] S5.3, the pulse component extracted by wavelet is expressed as : ; The discrete digital signal is represented as a vector .

[0065] Specifically, step S6 includes the following steps: S6.1, using CVMD and wavelet decomposition, the continuous smooth and impulse components are extracted as and In order to obtain accurate interference estimation, the following weighted fusion filter is proposed : ; in, , is the weight.

[0066] S6.2, order , using the criterion of minimizing the residual square error to determine the variables to be solved , as shown below: ; in, Express Taking the conjugate transpose, for The optimal solution of .

[0067] Through convex optimization, we get: ; ; ; in, and is the intermediate variable, is the conjugate operation, for The transpose of .

[0068] S6.3, after the fusion filter estimation, by Interference cancellation is performed and the target signal is extracted by combining the sparse reconstruction method. , ; in, is a sparse dictionary associated with the transmission waveform, is the penalty factor, To solve the target signal The optimal solution of .

[0069] S6.4, solved by the conjugate gradient method .

[0070] Specifically, in step S7: in order to improve the interference reconstruction accuracy and target detection probability, an alternating iteration method is proposed to estimate the interference signal and the target signal through iteration. Using the method provided by the present invention, complex modulated ISRJ can be effectively suppressed through fusion filtering, and target detection can be performed through sparse recovery.

[0071] In order to verify the effect of the present invention, the following simulation experiments and analyses are performed: The ISRJ data is simulated to verify the effectiveness of the proposed method. The experiment is divided into two parts: (1) The process of the method provided by the present invention is verified and compared with the single interference reconstruction method using CVMD or wavelet to demonstrate the improvement achieved by multi-model fusion filtering and alternating iteration.

[0072] (2) The statistical performance of the proposed method under different JSNRs was analyzed through Monte Carlo experiments, and typical ISRJ suppression methods were compared to verify the performance improvement of the proposed method.

[0073] The radar and jammer parameters are set as follows: carrier frequency is 5 GHz. Bandwidth is 1 MHz. Sampling frequency is 2.8 MHz. SNR is 15 dB and JSNR is 15 to 35 dB. The simulated echo of ISRJ is as follows: Figure 4 and Figure 5 As shown. It can be observed that the ISRJ with higher power covers Figure 4 The target signal in Figure 5The complex time-frequency distribution (TFD) with continuous smoothness and impulse characteristics is shown in the figure. Then, the method proposed in the present invention, as well as CVMD interference suppression and wavelet interference suppression, are used to process these simulated interference data. The processing results are shown in Figure 2. Figure 6-Figure 9 It can be seen that the proposed method effectively eliminates the interference and produces a clear time-frequency curve in TFD, as shown in Figure 6 As shown in Figure 2, the target can be detected after pulse compression. Figure 7 and Figure 8 As shown in Figure 1, the results using CVMD and wavelet methods still show obvious interference residuals due to the incomplete interference representation, and the target is still obscured by the residual interference after pulse compression. In addition, the residual JSNR (RJSNR), signal loss (SL), and interference reconstruction error (JRE) are quantitatively calculated in Table 1. The results show that the proposed method achieves the best reconstruction accuracy, the smallest interference residual, and the smallest target signal loss. The significant improvement can be attributed to the proposed method, which uses a fusion filtering framework with multiple models to address continuous smoothing and pulse characteristics, combined with an alternating iterative method. The proposed method improves interference reconstruction accuracy and target detection performance.

[0074] Table 1 Performance statistics

[0075] This embodiment evaluates the interference suppression performance under different JSNR conditions. The JSNR is set between 5dB and 35dB, and 500 Monte Carlo experiments are performed to statistically analyze the residual interference and signal loss after interference suppression. For comparison, method 1 is selected: <Band pass filter design against interrupted-sampling repeaterjamming based on time-frequency analysis> (Design of Interference Bandpass Filter for Interrupt Sampling Repeater Based on Time-Frequency Analysis); Method 2: <Interrupted-sampling repeater jamming suppression withone-dimensional semi-parametric signal decomposition> (《Interference suppression of interrupt sampling repeaters based on one-dimensional semi-parametric signal decomposition》) and method 3:<ISRJ Suppression Algorithm Based onWavelet Transform and Compressed Sensing Reconstruction> (ISRJ Suppression Algorithm Based on Wavelet Transform and Compressed Sensing Reconstruction) is used as the benchmark method. The results are as follows Figure 10-12 As shown in the figure, it shows that the method proposed by the present invention is superior to other methods. This superiority is attributed to the fact that the time-frequency filtering and wavelet filtering techniques cannot effectively distinguish the target signal from the interference signal when they overlap in the time-frequency domain, resulting in a large interference residue. In addition, the interference zeroing method suppresses the interference signal by setting the interference area to zero, but it also suppresses the target signal. In addition, when the false alarm rate is 10 −5 In the case of, the target detection probability is Fig.12 The method proposed in the present invention maintains strong detection performance at different JSNR levels, further verifying its effectiveness in ISRJ inhibition.

[0076] The present invention proposes an ISRJ suppression method based on multi-model fusion. The method can reconstruct and eliminate interference signals with high precision for complex modulation interference. Simulation experiments show that the method has significant improvement over traditional methods.

[0077] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. An ISRJ suppression method based on multi-model fusion, characterized in that: The specific steps include: S1, build ISRJ signal model; S2, extracts interference fragments using a fusion interference detection method based on Transformer and decision tree; S3, based on the extracted interference fragments, complex variational mode decomposition (CVMD) is used to extract the continuous components of ISRJ, and the continuous components of ISRJ are decomposed into band-limited intrinsic mode functions (BIMFs); S4, selecting an interference signal and a target signal based on the estimated interference signal-to-noise ratio, and reconstructing the selected interference signal; S5, reconstruct the interference signal using multi-scale wavelet decomposition; S6, fusing the interference signal after step S4 and step S5; S7, using the interference signal obtained in step S6 to eliminate the interference of the original signal, reconstructing the target signal using a sparse reconstruction algorithm, and then using the reconstructed target signal to eliminate the target signal in the original signal and reconstruct the interference signal, and repeating this process alternately to reconstruct the interference signal and the target signal.

2. According to the ISRJ suppression method based on multi-model fusion according to claim 1, it is characterized in that: Step S1 specifically includes the following steps: S1.1, assuming that the radar transmit waveform is , is modeled as a linear frequency modulation LFM signal; The ISRJ signal composed of fragments is expressed as : ； in, is the interference amplitude after power amplification, is a rectangular pulse function, For the The storage time of the interference fragments, is the interference fragment length; S1.2, modulate each interference segment, and the modulated ISRJ signal is expressed as : ； in, is the convolution operator, is the random modulation kernel; S1.3, if there is targets, the total received signal is expressed as : ； in, For the The range of the target, For the The time delay of the target echo is is Gaussian white noise; after sampling, Represented as a vector .

3. According to claim 1, the ISRJ suppression method based on multi-model fusion is characterized in that: Step S3 specifically includes the following steps: S3.1, Band-limited intrinsic mode functions BIMFs: ； ； in, is the center frequency, is the modal function, The decomposed BIMFs, is the number of BIMFs, is the target signal or the observed signal, is an exponential function, is the minimum value function; S3.2, the band-limited intrinsic mode functions BIMFs are solved using the augmented Lagrangian method with alternating direction multiplier method ADMM.

4. The ISRJ suppression method based on multi-model fusion according to claim 1, characterized in that: Step S4 specifically includes the following steps: S4.1, Interference signal reconstructed using selected BIMFs It is expressed as: ； in, is the support set of the selected BIMFs, estimated as: ； in, is the number of selected BIMFs components, is the signal length, are the power of the signal and the noise, is the threshold factor, for The square of the 2-norm of is the parameter minimum function; S4.2, using greedy algorithm to solve , the reconstructed interference signal The discrete digital signal is represented as a vector .

5. The ISRJ suppression method based on multi-model fusion according to claim 1, characterized in that: Step S5 specifically includes the following steps: S5.1, using the interference fragment obtained in step S2, the interference wavelet coefficients of different expansion and translation factors are expressed as : ； in, is the complex conjugate of the mother wavelet, Is a dilation factor and translation factor The wavelet kernel is expressed as: ； in, is the mother wavelet; S5.2, design an adaptive threshold to separate the pulse interference component and the target signal component as follows: ； ； in, is the threshold factor associated with JSRN, is the adjustment factor, is the processed wavelet coefficient, is a nonlinear function, is a sign function; S5.3, the pulse component extracted by wavelet is expressed as : ； The discrete digital signal is represented as a vector .

6. The ISRJ suppression method based on multi-model fusion according to claim 1, characterized in that: Step S6 specifically includes the following steps: S6.1, the following weighted fusion filter is proposed : ； in, , is the weight; S6.2, order , using the criterion of minimizing the residual square error to determine the variables to be solved , as shown below: ； in, Express Taking the conjugate transpose, for The optimal solution of Through convex optimization, we get ； ； ； in, and is the intermediate variable, is the conjugate operation, for The transpose of S6.3, by Interference cancellation is performed and the target signal is extracted by combining the sparse reconstruction method. , ； in, is a sparse dictionary associated with the transmission waveform, is the penalty factor, To solve the target signal The optimal solution of S6.4, solved by the conjugate gradient method .

Images