Spaceborne synthetic aperture radar scattering wave mutual interference suppression method and system based on matched filtering, storage medium and electronic equipment

Abstract

The invention discloses a spaceborne synthetic aperture radar scattering wave mutual interference suppression method and system based on matched filtering, a storage medium and electronic equipment, and the method comprises the following steps: estimating related parameters of mutual interference according to the prior information of an orbit and a time-frequency relation, and carrying out the range pulse compression of an interference signal in echo data; detecting whether the range pulse contains interference or not by adopting a maximum characteristic value sequence detection method; calculating a relative signal to interference ratio containing interference pulses; reconstructing and extracting interference signals under the conditions of high signal to interference plus noise ratio and low signal to interference plus noise ratio respectively by using a feature subspace projection method and a robust principal component analysis method; and filtering the echo data, and carrying out mutual interference inverse distance compression processing on the data so as to recover normal echoes without interference. According to the invention, the compression gain of mutual interference can be effectively utilized, mutual interference signals in synthetic aperture radar echoes can be extracted more effectively, and high-precision interference suppression is completed while useful signals are better protected.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a method, system, storage medium and electronic equipment for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering. Background Art

[0002] At present, Synthetic Aperture Radar (SAR) is an active remote sensing device. The SAR system can be carried by satellites and achieve high-resolution imaging of the earth's surface by virtue of its unique synthetic aperture technology. Compared with traditional optical remote sensing, satellite-borne SAR has the advantages of all-weather, all-day, and cloud penetration, and has been widely used in earth science research, disaster monitoring, environmental monitoring and other fields. However, with the rapid development of global communications, the wireless spectrum is becoming increasingly tight, and many electromagnetic devices share the same frequency band. Therefore, satellite-borne SAR will inevitably receive radio frequency interference in the electromagnetic environment during the mission. In recent years, with more and more SAR satellites launched, satellite-borne SARs in the same band may receive mutual radio frequency interference from other SAR satellites at the intersection of orbital operations. For example, the European Sentinel-1 in the C band had a long-term mutual interference with China's Gaofen-3 and Canada's Radarsat-2 during its mission. Mutual radio frequency interference will seriously deteriorate the quality of SAR images due to its long duration, large bandwidth and unstable pulse energy, greatly affecting subsequent product applications and bringing great challenges to interference suppression.

[0003] Interference suppression is an effective means to remove interference effects and obtain high-quality SAR image data. In order to reduce the negative impact of interference on SAR systems, research teams at home and abroad have conducted extensive research on interference suppression in SAR data and proposed a variety of classic interference suppression methods. These methods can be mainly divided into three categories: parametric, semi-parametric and non-parametric methods.

[0004] The core idea of ​​the parameterization method is to establish an accurate mathematical model of the interference signal, so that the interference signal can be separated from the SAR echo data to achieve the suppression effect. Typical parameterization methods include maximum likelihood estimation, least squares estimation, parameter maximum likelihood and least mean square estimation algorithms. Zhou Feng and other researchers proposed methods such as regularized time-frequency filtering and second-order multi-synchronous compression transform to suppress broadband interference. Yang Huizhang and others proposed a two-dimensional spectrum filtering method using a parametric linear frequency modulation signal model, which can suppress the mutual interference of direct waves between SAR satellites. Yang et al. also proposed a block subspace filtering method to suppress interference in single-view complex SAR images. This method can suppress various common radio frequency interference in spaceborne SAR.

[0005] Semi-parametric methods suppress interference by converting complex signal separation problems into hyperparameter optimization problems. For example, Nguyen et al. adopted a sparse-driven optimization strategy to separate interference signals from SAR data; Liu Hongqing and other researchers used sparse representation reconstruction algorithms in the range-time-frequency domain to achieve interference suppression, providing new ideas for the design of subsequent related algorithms. Huang Yan et al. proposed a series of "low-rank + sparse" models, which showed good suppression effects. The core of this type of method is to construct optimization models of different interference types based on the low-rank and sparse characteristics of interfering SAR echoes to achieve separation of interference and SAR signals.

[0006] Non-parametric methods mainly suppress interference by utilizing the energy characteristics of interference signals. At present, classic non-parametric methods include notch filters, subband cancellation methods, and subspace projection methods. The notch filter method suppresses interference by setting the interference component to zero in a certain domain; the subband cancellation method mainly acts on the focused image data and uses the power spectrum to cancel interference; the subspace projection method can be used in both the time domain and the frequency domain, and performs well in processing narrowband and broadband interference, and can better protect useful signals, but it will produce abnormal responses due to over-mapping of the subspace when the interference energy is too strong.

[0007] In summary, in order to obtain high-quality SAR images, it is of great significance to carry out high-precision suppression research on SAR scattered wave mutual interference. Effective mutual interference suppression methods are crucial to improving the survivability of SAR systems. Summary of the invention

[0008] The purpose of the present invention is to provide a method, system, storage medium and electronic equipment for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering.

[0009] The technical solution adopted by the present invention is:

[0010] A method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering comprises the following steps:

[0011] Step S101, estimating relevant parameters of mutual interference according to track prior information and time-frequency relationship, and performing range-direction pulse compression on the interference signal in the original echo data;

[0012] Step S102: using a maximum eigenvalue sequence detection method to detect whether the range pulse contains interference;

[0013] Step S103, calculating the relative signal-to-interference ratio of the interference pulse based on the interference detection result in step S102;

[0014] Step S104: classify the pulses containing interference according to the relative signal-to-interference ratio to obtain two parts of data, namely, the interference signal under the condition of high signal-to-interference-noise ratio and the interference signal under the condition of low signal-to-interference-noise ratio;

[0015] Step S105, reconstructing the interference signal under high signal-to-interference-noise ratio conditions using a characteristic subspace projection method;

[0016] Step S106: extracting interference signals under low signal-to-interference-noise ratio conditions using a robust principal component analysis method;

[0017] Step S107, according to the interference signal reconstructed and extracted in step S105 and step S106, filter it from the compressed synthetic aperture radar echo data, and finally perform inverse range compression processing on the interference filtered data to restore it to a normal echo without interference.

[0018] Step S101 is specifically as follows:

[0019] The mutual interference signal is very similar to the SAR signal itself. m is the constant term coefficient, B m represents the bandwidth of mutual interference, f represents the frequency, and f c and f m are the carrier frequencies of the SAR satellite and the interference source satellite, respectively, m is the interference modulation frequency, t m is the delay from transmission to reception of interference; j is an imaginary unit, and the small symbols in the subscripts are used to distinguish interference from target signals and have no actual meaning. The subscript c is the target signal, and the subscript m is the interference signal. The mutual interference signal spectrum can be expressed as:

[0020]

[0021] The frequency domain matched filter design for mutual interference can eliminate the quadratic phase in the model, which is expressed as

[0022]

[0023] Estimation of carrier frequency f based on orbital data of spaceborne SAR and interference source satellite when interference occurs m , related parameters B m and K m The estimation is performed by combining the time-frequency relationship of the strong interference in the echo; Generally speaking, the carrier frequencies of the two SAR satellites f c and f m The sizes are similar or consistent; the estimated parameters are used to compress the range of the SAR signal containing interference; ignoring the π / 4 phase term, the range compression of the mutual interference signal can be expressed as:

[0024]

[0025] The time domain model of the SAR echo after frequency domain matching filtering of the interference is as follows:

[0026]

[0027] Where D c ,t c , K c and B c They represent the amplitude, delay, modulation rate and bandwidth of the normal SAR echo signal, respectively. The subscript c is the target signal, and the subscript m is the interference signal. The modeling after the mutual interference distance compression processing shows that the target SAR signal is not successfully compressed like the mutual interference signal due to the mismatch of the filter related parameters. The distance compression effect of mutual interference is related to the carrier frequency offset of the two SAR satellites. Generally speaking, the carrier frequency f that meets the mutual interference formation conditions is c and f m If they are similar or consistent, if they are the same or their slight differences are ignored, the mutual interference distance compression effect is best.

[0028] In step S102, the maximum eigenvalue sequence detection method is used to detect whether the range pulse contains interference, specifically:

[0029] The maximum eigenvalue sequence detection method is used here to detect whether the pulse contains mutual interference: the maximum eigenvalue sequence is obtained by matrix eigenvalue decomposition, and then the threshold is set to obtain the final detection result of whether there is mutual interference; based on the signal compressed by the mutual interference distance, N a and N r Respectively represent the number of sampling points of SAR signal in azimuth and range, assuming i = 1, 2, ..., N a and z = 1, 2, ..., N r , with S i (γ z ) represents the zth sampling point of the ith range pulse signal, then the Hankel matrix corresponding to this pulse can be expressed as:

[0030]

[0031] Where M = N r -N+1, based on which the covariance matrix C corresponding to this pulse can be constructed m =H m H m H, N is the dimension of the covariance matrix, which is usually set to 16 to 128 according to the actual processing situation; the constructed covariance matrix is ​​subjected to eigenvalue decomposition, and the maximum eigenvalue obtained by decomposition is taken; the above processing is performed pulse by pulse to obtain a sequence composed of the maximum eigenvalues ​​of all pulses, which is expressed as

[0032]

[0033] In E max In the sequence, the eigenvalues ​​corresponding to pulses without mutual interference are relatively small and fluctuate gently, while the maximum eigenvalues ​​corresponding to pulses with mutual interference are relatively abnormal. All distance pulses are classified into those with or without mutual interference, and the robust K-means clustering algorithm is used to perform binary segmentation on the maximum eigenvalue sequence to obtain the final detection result:

[0034]

[0035] Where σ represents the threshold of cluster adaptive segmentation, a value of 1 means that the pulse contains mutual interference, and a value of 0 means that it does not contain mutual interference.

[0036] In step S103, based on the interference detection result of step S102, the relative signal-to-interference ratio of the interference pulse is calculated, specifically:

[0037] According to the correlation of SAR signals and the interference detection results, the average power of all pulses without mutual interference is calculated, which can be used as a benchmark for further judging the signal-to-interference-noise ratio of pulses with interference. Assuming that the number of pulses without mutual interference is N m , then the number of pulses containing mutual interference can be expressed as N a -N m , then the average power of the signal without mutual interference is calculated as follows:

[0038]

[0039] Where ε represents the sequence number of the pulse containing mutual interference; P s It can be used as the basis for judging the signal-to-interference-noise ratio, because when the background noise is ignored, P s represents only the power of the useful signal, while the average power of the kth pulse containing both mutual interference and the useful signal can be expressed as

[0040]

[0041] P s Taking the signal power without interference as a reference, the relative signal-to-interference ratio of the kth pulse can be calculated as follows:

[0042]

[0043] Perform the above relative signal-to-interference ratio calculation for each pulse with mutual interference one by one.

[0044] In step S105, the interference signal under high signal-to-interference-plus-noise ratio conditions is reconstructed using the eigen-subspace projection method, specifically:

[0045] In the case where the R(k) value is marked as 1, that is, all pulse data corresponding to high signal-to-interference-plus-noise ratio, perform mutual interference reconstruction one by one using the subspace projection method; the subspace projection method performs eigenvalue decomposition on the covariance matrix constructed by the pulses, and then projects the interference and the target signal into different subspaces, which can be expressed as

[0046] C m = H m H m H = UΛU H

[0047] where Λ = diag(λ 1 , λ 2 , …, λ N ), λ corresponds to the eigenvalue sequence and is sorted from large to small; U = [μ 1 , μ 2 , …, μ N is the eigenvector corresponding to each eigenvalue; according to the maximum eigenvalue sequence in the interference detection step, first eliminate the eigenvalues containing interference pulses, and then fit the eigenvalues without interference to obtain the dynamic threshold, and mark the a (1 < a < N) eigenvalues higher than the threshold as the eigenvalues corresponding to interference, and the eigenvectors are the same by analogy; it is expressed as

[0048] Λ a = diag(λ 1 , λ 2 , …, λ a )

[0049] U a = [μ 1 , μ 2 , …, μ a

[0050] The interference subspace is formed by expanding the eigenvectors, which is expressed as span(U a ); perform interference subspace projection based on the Hankel matrix H m :

[0051] S a = U a U a H H m

[0052] The interference subspace S​a It contains all the information of mutual interference under high signal-to-interference-noise ratio conditions; by rearranging it, a new vector can be obtained, which is the interference reconstruction result under high signal-to-interference-noise ratio.

[0053] In step S106, the interference signal under the low signal-to-interference-noise ratio condition is extracted using a robust principal component analysis method, specifically:

[0054] Based on the sparsity of echo signals, the mutual interference extraction problem can be transformed into an optimization problem of a semi-parametric method: under low signal-to-interference-noise ratio, the amplitude of the SAR target signal and the mutual interference signal differ greatly, so the overall SAR signal can be modeled as a low-rank smooth background, while the mutual interference completed by range compression can be modeled as a sparse target with abnormal energy; thus, the mutual interference is sparsely constrained and the semi-parametric optimization problem is solved; for the observed echo signal matrix S, ignoring the background noise, the above target optimization problem can be expressed as

[0055]

[0056] where λ represents a hyperparameter, ‖·‖ 0 For L 0 norm; because the target signal S r It has linear correlation and can be projected into a lower-dimensional subspace when the signal-to-noise ratio is low, expressed as a low-rank matrix. However, the matrix rank is non-convex, and it is difficult to perform the corresponding optimization solution, which extends to its convex approximate optimization solution, that is, the nuclear norm ‖·‖ * , the above optimization problem can be updated as

[0057]

[0058] in‖·‖ 1 For L 1 norm, whose minimization is conducive to sparse constraints on mutual interference signals; through the balance of low-rank terms and sparse terms by the weight parameter λ, when the optimization model meets the convergence condition or reaches the maximum number of iterations, strong energy mutual interference can be effectively extracted under the condition of low signal-to-interference-noise ratio of echo data.

[0059] In step S107, according to the interference signals extracted in step S105 and step S106, the interference signals are filtered out from the spaceborne synthetic aperture radar echo data, and finally the data is subjected to interference inverse range compression processing to restore to normal echoes without interference, specifically:

[0060] Combine the reconstruction and extraction results of mutual interference under different signal-to-noise ratio data, arrange them according to the original pulse order, fill 0 with pulses without mutual interference, and obtain the final extracted mutual interference echo; extract the mutual interference from the interference-compressed echo signal S MFThe mutual interference is eliminated, that is, the mutual interference is suppressed, which is expressed as

[0061]

[0062] Among them, M E represents the extracted mutual interference signal matrix, To remove the final signal matrix after mutual interference; at the same time, additional processing is required to restore the target signal affected by the matched filter, because the range compression processing of the mutual interference introduces unnecessary phases to the useful SAR signal; according to the design of the matched filter, the corresponding phase needs to be eliminated to express The distance spectrum of

[0063]

[0064] Among them, M F * (f) represents the matched filter M F The complex conjugate of (f) and then S r (f) Data recovery can be completed by performing inverse Fourier transform to obtain the final SAR signal after mutual interference suppression.

[0065] A spaceborne synthetic aperture radar scattering wave mutual interference suppression system based on matched filtering, comprising:

[0066] An interference signal pulse compression unit, configured to estimate relevant parameters of interference and perform range-direction pulse compression on the interference signal in the echo data;

[0067] An interference detection unit is configured to detect whether all range pulses contain interference using a maximum eigenvalue sequence detection method;

[0068] A relative signal-to-interference ratio calculation unit, configured to calculate the relative signal-to-interference ratios of all interference-containing pulses by power comparison based on the interference detection result;

[0069] The interference signal-to-interference-noise ratio classification unit is configured to classify the interference pulses according to the obtained relative signal-to-interference ratio to obtain two parts of data: high signal-to-interference-noise ratio and low signal-to-interference-noise ratio;

[0070] An interference extraction unit, configured to reconstruct and extract interference signals under high signal-to-interference-noise ratio and low signal-to-interference-noise ratio conditions using a feature subspace projection method and a robust principal component analysis method respectively;

[0071] The interference suppression and data recovery unit is configured to filter out the extracted interference signal from the echo data, and then perform mutual interference inverse range compression processing on the data to restore it to a normal echo without interference.

[0072] A storage medium stores a plurality of programs, wherein the programs are loaded and executed by a processor for executing a method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering.

[0073] An electronic device includes a storage medium and a processor; the processor is suitable for executing various programs; a memory is used to store multiple programs; it is characterized in that the memory implements the method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering when executing the program on the processor. In the method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering provided by the present invention, firstly, the relevant parameters of mutual interference are estimated according to orbital prior information and time-frequency relationship, and the interference signal in the echo data is compressed in the range direction; then, the maximum eigenvalue sequence detection method is used to detect whether the range pulse contains interference; based on the correlation of the echo signal and in combination with the interference detection result, the relative signal-to-interference ratio of the interference pulse is calculated; the interference pulse is classified by the relative signal-to-interference ratio to obtain two parts of data with high signal-to-interference-noise ratio and low signal-to-interference-noise ratio; the characteristic subspace projection method and the robust principal component analysis method are used to reconstruct and extract the interference signal under the conditions of high signal-to-interference-noise ratio and low signal-to-interference-noise ratio respectively; finally, it is filtered out from the echo data, and the data is subjected to mutual interference inverse range compression processing to restore to a normal echo without interference. The present invention can effectively utilize the compression gain of mutual interference, thereby more effectively extracting the mutual interference signal in the synthetic aperture radar echo, and achieving high-precision interference suppression while better protecting the useful signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0075] Figure 1 It is a schematic diagram of the process of the present invention;

[0076] Figure 2 It is a characteristic diagram of the scattered wave mutual interference signal of the present invention;

[0077] Figure 3 The signal bandwidth distribution diagram of GF-3 and Sentinel-1 described in the present invention;

[0078] Figure 4 It is a schematic diagram of the process of the present invention;

[0079] Figure 5 This is a comparison diagram of the effects before and after the actual use of the present invention. DETAILED DESCRIPTION

[0081] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as a part of an embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desired that the present application includes such modifications and variations within the scope of the appended claims and their equivalents.

[0082] Exemplary Methods

[0083] like Figure 1 As shown, the method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering includes:

[0084] Step S101, estimating interference related parameters, and performing range-direction pulse compression on interference signals in echo data;

[0085] Specifically, the relevant parameters of mutual interference are estimated based on the orbit prior information and the time-frequency relationship, and the interference signal in the echo data is pulse compressed in the range direction, specifically:

[0086] The distance compression of the mutual interference signal in the echo can concentrate the energy of each pulse into a shorter time, thereby increasing the peak energy of the mutual interference signal on the time axis, making the mutual interference originally distributed in the entire pulse width more concentrated, thereby improving the resolution of mutual interference, which is very beneficial to the subsequent high-precision extraction of mutual interference.

[0087] The mutual interference signal is very similar to the SAR signal itself. m is the constant term coefficient, B m represents the bandwidth of mutual interference, f represents the frequency, and f c and f m are the carrier frequencies of the SAR satellite and the interference source satellite, respectively, m is the interference modulation frequency, t m is the delay from transmission to reception of interference. The spectrum of the mutual interference signal can be expressed as:

[0088]

[0089] The frequency domain matched filter design for mutual interference can eliminate the quadratic phase in the model, which is expressed as

[0090]

[0091] Estimation of carrier frequency f based on orbital data of spaceborne SAR and interference source satellite when interference occursm , related parameters B m and K m The estimation is performed by combining the time-frequency relationship of the strong interference in the echo. c and f m The sizes are similar or consistent. The estimated parameters are used to perform range compression on the SAR signal containing interference. Ignoring the π / 4 phase term, the range compression of the mutual interference signal can be expressed as

[0092]

[0093] The time domain model of the SAR echo after frequency domain matching filtering of the interference is as follows:

[0094]

[0095] Where D c ,t c , K c and B c They represent the amplitude, delay, modulation rate and bandwidth of the normal SAR echo signal respectively. The modeling after mutual interference distance compression processing shows that the target SAR signal is not successfully compressed like the mutual interference signal due to the mismatch of filter related parameters. The distance compression effect of mutual interference is related to the carrier frequency offset of the two SAR satellites. Generally speaking, the carrier frequency f that meets the mutual interference formation conditions is c and f m If they are similar or consistent, if they are the same or their slight differences are ignored, the mutual interference distance compression effect is best.

[0096] like Figure 2 The echo and image characteristics of the scattered wave mutual interference signal shown in the figure, ultra-wideband, long duration and unstable energy, etc. make some traditional methods unable to effectively detect interference, and traditional methods will lose a lot of useful information. The traditional suppression method will lose a lot of useful information. The spaceborne synthetic aperture radar scattered wave mutual interference suppression method based on matched filtering can overcome this problem. The interference source is Gaofen-3 and the SAR satellite is Sentinel-1.

[0097] Step S102: using a maximum eigenvalue sequence detection method to detect whether the range pulse contains interference;

[0098] Specifically, the maximum eigenvalue sequence detection method is used here to detect whether the pulse contains mutual interference. The maximum eigenvalue sequence method benefits from the fact that the energy between the pulse containing mutual interference and the normal pulse is usually inconsistent, and can obtain high-precision detection results. It is not limited by bandwidth and has high robustness. With the support of the mutual interference distance compression operation, the accuracy of the maximum eigenvalue sequence method can also be improved to a certain extent.

[0099] The maximum eigenvalue sequence is obtained by matrix eigenvalue decomposition, and then the threshold is set to obtain the final detection result of whether there is mutual interference. Based on the signal compressed by the mutual interference distance, use N a and N r Respectively represent the number of sampling points of SAR signal in azimuth and range, assuming i = 1, 2, ..., N a and z = 1, 2, ..., N r , with S i (γ z ) represents the zth sampling point of the ith range pulse signal, then the Hankel matrix corresponding to this pulse can be expressed as:

[0100]

[0101] Where M = N r -N+1, based on which the covariance matrix C corresponding to this pulse can be constructed m =H m H m H , N is the dimension of the covariance matrix, which is usually set to 16 to 128 in actual processing. The constructed covariance matrix is ​​subjected to eigenvalue decomposition, and the maximum eigenvalue obtained by decomposition is taken. The above process is performed pulse by pulse to obtain a sequence composed of the maximum eigenvalues ​​of all pulses, which is expressed as

[0102]

[0103] In E max In the sequence, the eigenvalues ​​corresponding to pulses without mutual interference are relatively small and fluctuate gently, while the maximum eigenvalues ​​corresponding to pulses with mutual interference are relatively abnormal. All distance pulses are classified as having or not interfering with each other, and the robust K-means clustering algorithm is used to perform binary segmentation on the maximum eigenvalue sequence to obtain the final detection result:

[0104]

[0105] Where σ represents the threshold of cluster adaptive segmentation, a value of 1 means that the pulse contains mutual interference, and a value of 0 means that it does not contain mutual interference.

[0106] Step S103, calculating the relative signal-to-interference ratio of the interference pulse based on the interference detection result in step S102;

[0107] Specifically, according to the correlation of SAR signals and the interference detection results, the average power of all pulses without mutual interference is calculated, which can be used as a benchmark for further judging the signal-to-noise ratio of pulses with interference. Assume that the number of pulses without mutual interference is N m , then the number of pulses containing mutual interference can be expressed as Na -N m , then the average power of the signal without mutual interference is calculated as follows:

[0108]

[0109] Where ε represents the sequence number of the pulse containing mutual interference. s It can be used as the basis for judging the signal-to-interference-noise ratio, because when the background noise is ignored, P s represents only the power of the useful signal, while the average power of the kth pulse containing both mutual interference and the useful signal can be expressed as

[0110]

[0111] P s Taking the signal power without interference as a reference, the relative signal-to-interference ratio of the kth pulse can be calculated as follows:

[0112]

[0113] The above relative signal-to-interference ratio calculation is performed one by one for all pulses containing mutual interference.

[0114] Step S104, classifying the interference pulses according to the relative signal-to-interference ratio to obtain two parts of data: high signal-to-interference-noise ratio and low signal-to-interference-noise ratio;

[0115] Specifically, the calculated signal-to-interference ratio is segmented by threshold, which is expressed as

[0116]

[0117] R(k) is the final classification result, which classifies pulses containing mutual interference into two categories, with values ​​of 1 and 0 corresponding to high signal-to-interference-noise ratio data and low signal-to-interference-noise ratio data. Represents the classification threshold, which is an empirical value obtained from a large number of experiments.

[0118] like Figure 3 As shown in the figure, according to the bandwidth overlap of two SAR satellites in the same band, it can be seen that the mutual interference signals are usually large bandwidth signals, and the accuracy of previous methods such as frequency domain notching and sub-band cancellation will drop sharply. Combined with the energy instability of interference, the advantages of semi-parametric robust principal component analysis method and non-parametric subspace projection method are fully utilized to achieve high-precision interference suppression while better protecting useful signals.

[0119] Step S105, reconstructing the interference signal under high signal-to-interference-noise ratio conditions using a characteristic subspace projection method;

[0120] Specifically, when the R(k) value is marked as 1, that is, all pulse data corresponding to high signal-to-interference-plus-noise ratio, the mutual interference reconstruction is performed one by one using the subspace projection method. The subspace projection method performs eigenvalue decomposition on the covariance matrix constructed by the pulses, and then projects the interference and the target signal into different subspaces, which can be expressed as

[0121] C m = H m H m H = UΛU H

[0122] where Λ = diag(λ 1 , λ 2 , …, λ N ), λ corresponds to the eigenvalue sequence and is sorted from large to small. U = [μ 1 , μ 2 , …, μ N is the eigenvector corresponding to each eigenvalue. According to the maximum eigenvalue sequence in the interference detection step, first eliminate the eigenvalues containing interference pulses, and then fit the eigenvalues without interference to obtain the dynamic threshold. Mark the a (1 < a < N) eigenvalues higher than the threshold as the eigenvalues corresponding to the interference, and the eigenvectors are the same by analogy. It is expressed as

[0123] Λ a = diag(λ 1 , λ 2 , …, λ a )

[0124] U a = [μ 1 , μ 2 , …, μ a

[0125] The interference subspace is formed by expanding the eigenvectors, which is expressed as span(U a ). Based on the Hankel matrix H m , perform interference subspace projection:

[0126] S a = U a U a H H m

[0127] The interference subspace S a contains all the information of the mutual interference under the condition of high signal-to-interference-plus-noise ratio. Rearrange it to obtain a new vector, which is the interference reconstruction result under high signal-to-interference-plus-noise ratio.

[0128] ​Step S106: extracting interference signals under low signal-to-interference-noise ratio conditions using a robust principal component analysis method;

[0129] Specifically, based on the sparsity of the echo signal, the mutual interference extraction problem can be transformed into an optimization problem of a semi-parametric method. Under low signal-to-interference-noise ratio, the amplitude of the SAR target signal and the mutual interference signal differ greatly, so the overall SAR signal can be modeled as a low-rank smooth background, while the mutual interference completed by distance compression can be modeled as a sparse target with abnormal energy. In this way, the mutual interference is sparsely constrained and the semi-parametric optimization problem is solved. For the observed echo signal matrix S, ignoring the background noise, the above target optimization problem can be expressed as

[0130]

[0131] where λ represents a hyperparameter, ‖·‖ 0 For L 0 Norm. Since the target signal S r It has linear correlation and can be projected into a lower-dimensional subspace when the signal-to-noise ratio is low, expressed as a low-rank matrix. However, the matrix rank is non-convex, and it is difficult to perform the corresponding optimization solution, which extends to its convex approximate optimization solution, that is, the nuclear norm ‖·‖ * , the above optimization problem can be updated as

[0132]

[0133] in‖·‖ 1 For L 1 norm, and its minimization is conducive to sparse constraints on mutual interference signals. The weight parameter λ balances the low-rank term and the sparse term. When the optimization model meets the convergence condition or reaches the maximum number of iterations, the strong energy mutual interference can be effectively extracted under the condition of low signal-to-noise ratio of the echo data.

[0134] Step S107, according to the interference signal extracted in step S105 and step S106, filter it from the spaceborne synthetic aperture radar echo data, and finally perform interference inverse range compression processing on the data to restore it to a normal echo without interference;

[0135] Specifically, the reconstruction and extraction results of mutual interference under different signal-to-noise ratio data are combined, and they are arranged according to the original pulse order. The pulses without mutual interference are filled with 0 to obtain the final extracted mutual interference echo. The extracted mutual interference is extracted from the interference-compressed echo signal S MF The mutual interference is eliminated, that is, the mutual interference is suppressed, which is expressed as

[0136]

[0137] Among them, M Erepresents the extracted mutual interference signal matrix, This is the final signal matrix after removing the mutual interference. At the same time, additional processing is required to recover the target signal affected by the matched filter, because the range compression processing of the mutual interference introduces unnecessary phases to the useful SAR signal. According to the design of the matched filter, the corresponding phase needs to be eliminated to express The distance spectrum of

[0138]

[0139] Among them, M F * (f) represents the matched filter M F The complex conjugate of (f) and then S r (f) Data recovery can be completed by performing inverse Fourier transform to obtain the final SAR signal after mutual interference suppression.

[0140] like Figure 4 As shown in the figure, it is a schematic diagram of the process processing of the spaceborne synthetic aperture radar scattered wave mutual interference method based on matched filtering. This method starts from the original echo containing interference, and through interference distance compression, interference detection, interference extraction and suppression, and finally echo data recovery, it can effectively complete the suppression of scattered wave mutual interference between spaceborne SARs.

[0141] The comparison of the effect before and after interference suppression is as follows Figure 5 As shown in the figure, the experimental data is the real data of Sentinel-1, which is affected by the mutual interference of scattered waves from GF-3. By comparison, it can be seen that after being processed by the present invention, the SAR image originally suppressed by the cloud-like mutual interference artifact becomes clearly visible, the cloud-like effect of mutual interference almost disappears, and the quality of the SAR image is significantly improved. The mountains covered by white artifacts in the figure are clearly highlighted after interference suppression, and the application value of subsequent SAR images is significantly improved.

[0142] Exemplary Systems

[0143] The application embodiment also provides a spaceborne synthetic aperture radar scattering wave mutual interference suppression system based on matched filtering, including: an interference signal pulse compression unit, configured to estimate the relevant parameters of interference, and perform range pulse compression on the interference signal in the echo data; an interference detection unit, configured to use the maximum eigenvalue sequence detection method to detect whether all range pulses contain interference; a relative signal-to-interference ratio calculation unit, configured to calculate the relative signal-to-interference ratio of all interference-containing pulses by power comparison based on the interference detection result; an interference signal-to-interference-noise ratio classification unit, configured to classify the interference-containing pulses by the obtained relative signal-to-interference ratio to obtain two parts of data with high signal-to-interference-noise ratio and low signal-to-interference-noise ratio; an interference extraction unit, configured to use the characteristic subspace projection method and the robust principal component analysis method to reconstruct and extract the interference signal under high signal-to-interference-noise ratio and low signal-to-interference-noise ratio conditions. An interference suppression and data recovery unit, configured to filter the extracted interference signal from the echo data, and then perform mutual interference inverse range compression processing on the data to restore it to a normal echo without interference.

[0144] A space-borne synthetic aperture radar scattered wave mutual interference suppression system based on matched filtering provided in an embodiment of the present application can implement any of the above-mentioned steps and processes of space-borne synthetic aperture radar scattered wave mutual interference suppression based on matched filtering, and achieve the same technical effect, which will not be repeated here one by one.

[0145] Exemplary Devices

[0146] The present application provides an electronic device, including a storage medium and a processor, the processor being suitable for executing various programs; and a memory being used to store multiple programs; and characterized in that when the memory executes the program on the processor, the method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering is implemented.

[0147] Since the steps of a method for suppressing mutual interference of scattered waves of a space-borne synthetic aperture radar based on matched filtering have been introduced in detail in the specific implementation method example, they will not be described in detail here.

[0148] The processor includes a central processing unit (CPU), a network processor (NP), etc. It can also be a digital signal processor, an application-specific integrated circuit, a readily available programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0149] The processor can be specifically configured as follows: first, the relevant parameters of mutual interference are estimated according to the orbit prior information and the time-frequency relationship, and the interference signal in the echo data is compressed in the range direction; then the maximum eigenvalue sequence detection method is used to detect whether there is interference in the range direction pulse; based on the correlation of the echo signal, combined with the interference detection result, the relative signal-to-interference ratio of the interference pulse is calculated; the interference pulse is classified by the relative signal-to-interference ratio to obtain two parts of data with high signal-to-interference-noise ratio and low signal-to-interference-noise ratio; the characteristic subspace projection method and the robust principal component analysis method are used to reconstruct and extract the interference signal under the conditions of high signal-to-interference-noise ratio and low signal-to-interference-noise ratio respectively; finally, the interference signal is filtered out from the echo data, and the data is subjected to mutual interference inverse range compression processing to restore it to a normal echo without interference. Through the present invention, the compression gain of mutual interference can be effectively utilized, and the mutual interference signal in the synthetic aperture radar echo can be more effectively extracted, and high-precision interference suppression can be achieved while better protecting the useful signal.

[0150] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0151] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as a computer code originally stored in a remote recording medium or a non-temporary machine storage medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, a method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0152] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and constraints involved in the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0153] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0154] The device and system embodiments described above are merely illustrative, wherein the units not shown as separate may or may not be physically separated, and the units not shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0155] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0156] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0157] Note that the above are only preferred embodiments of the present invention and the principles of the application technology. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention is described in more detail through the above embodiments, the present invention is not limited to the specific embodiments described herein, and may include more other effective embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering, characterized in that: The steps include: Step S101, estimating relevant parameters of mutual interference according to track prior information and time-frequency relationship, and performing range-direction pulse compression on the interference signal in the original echo data; Step S102: using a maximum eigenvalue sequence detection method to detect whether the range pulse contains interference; Step S103, calculating the relative signal-to-interference ratio of the interference pulse based on the interference detection result in step S102; Step S104: classify the pulses containing interference according to the relative signal-to-interference ratio to obtain two parts of data, namely, the interference signal under the condition of high signal-to-interference-noise ratio and the interference signal under the condition of low signal-to-interference-noise ratio; Step S105: reconstructing the interference signal under high signal-to-interference-noise ratio conditions using a characteristic subspace projection method; Step S106: extracting interference signals under low signal-to-interference-noise ratio conditions using a robust principal component analysis method; Step S107, filtering out the interference signal obtained after reconstruction and extraction in step S105 and step S106 from the compressed synthetic aperture radar echo data, and finally performing inverse range compression processing on the data after interference filtering to restore it to a normal echo without interference.

2. The method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering according to claim 1, characterized in that: Step S101 includes the following steps: The mutual interference signal is very similar to the SAR signal itself. m is the constant term coefficient, B m represents the bandwidth of mutual interference, f represents the frequency, and f c and f m are the carrier frequencies of the SAR satellite and the interference source satellite, respectively, m is the interference modulation frequency, t m The delay from transmission to reception for interference; j is an imaginary unit. The small symbols in the subscripts are used to distinguish interference from target signals and have no actual meaning. The subscript c is the target signal and the subscript m is the interference signal. The spectrum of the mutual interference signal can be expressed as: The frequency domain matched filter design for mutual interference can eliminate the quadratic phase in the model, which is expressed as Estimation of carrier frequency f based on orbital data of spaceborne SAR and interference source satellite when interference occurs m , related parameters B m and K m The estimation is performed by combining the time-frequency relationship of the strong interference in the echo; Generally speaking, the carrier frequencies of the two SAR satellites f c and f m The sizes are similar or consistent; the estimated parameters are used to compress the range of the SAR signal containing interference; ignoring the π / 4 phase term, the range compression of the mutual interference signal can be expressed as: The time domain model of the SAR echo after frequency domain matching filtering of the interference is as follows: Where D c ,t c , K c and B c They represent the amplitude, delay, modulation rate and bandwidth of the normal SAR echo signal, respectively. The subscript c is the target signal, and the subscript m is the interference signal. The modeling after the mutual interference distance compression processing shows that the target SAR signal is not successfully compressed like the mutual interference signal due to the mismatch of the filter related parameters. The distance compression effect of mutual interference is related to the carrier frequency offset of the two SAR satellites. Generally speaking, the carrier frequency f that meets the mutual interference formation conditions is c and f m If they are similar or consistent, if they are the same or their slight differences are ignored, the mutual interference distance compression effect is best.

3. The method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering according to claim 1, characterized in that: The step S102 includes the following steps: The maximum eigenvalue sequence is obtained by matrix eigenvalue decomposition, and then the threshold is set to obtain the final detection result of whether there is mutual interference; based on the signal compressed by the mutual interference distance, N a and N r Respectively represent the number of sampling points of SAR signal in azimuth and range, assuming i = 1, 2, ..., N a and z = 1, 2, ..., N r ,, with S i (γ z ) represents the zth sampling point of the ith range pulse signal, then the Hankel matrix corresponding to this pulse can be expressed as: Where M = N r -N+1, based on which the covariance matrix C corresponding to this pulse can be constructed m =H m H m H , N is the dimension of the covariance matrix; the constructed covariance matrix is ​​subjected to eigenvalue decomposition, and the maximum eigenvalue obtained by decomposition is taken; the above process is performed pulse by pulse to obtain a sequence composed of the maximum eigenvalues ​​of all pulses, which is expressed as In E max In the sequence, the eigenvalues ​​corresponding to pulses without mutual interference are relatively small and fluctuate gently, while the maximum eigenvalues ​​corresponding to pulses with mutual interference are relatively abnormal. All distance pulses are classified into those with or without mutual interference, and the robust K-means clustering algorithm is used to perform binary segmentation on the maximum eigenvalue sequence to obtain the final detection result: Where σ represents the threshold of cluster adaptive segmentation, a value of 1 means that the pulse contains mutual interference, and a value of 0 means that it does not contain mutual interference.

4. The method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering according to claim 1, characterized in that: The step S103 specifically includes the following steps: According to the correlation of SAR signals and the interference detection results, the average power of all pulses without mutual interference is calculated, which can be used as a benchmark for further judging the signal-to-interference-noise ratio of pulses with interference. Assuming that the number of pulses without mutual interference is N m , then the number of pulses containing mutual interference can be expressed as N a -N m , then the average power of the signal without mutual interference is calculated as follows: Where ε represents the sequence number of the pulse containing mutual interference; P s It can be used as the basis for judging the signal-to-interference-noise ratio, because when the background noise is ignored, P s represents only the power of the useful signal, while the average power of the kth pulse containing both mutual interference and the useful signal can be expressed as P s Taking the signal power without interference as a reference, the relative signal-to-interference ratio of the kth pulse can be calculated as follows: The above relative signal-to-interference ratio calculation is performed one by one for all pulses containing mutual interference.

5. The method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering according to claim 1, characterized in that: The step S105 specifically includes the following steps: When the R(k) value is marked as 1, that is, all pulse data corresponding to high signal-to-interference-noise ratio are reconstructed one by one using the subspace projection method; the subspace projection method performs eigenvalue decomposition on the covariance matrix constructed by the pulse, and then projects the interference and target signals into different subspaces, which are expressed as C m =H m H m H =UΛU H where Λ = diag(λ1, λ2, …, λ N ), λ corresponds to the eigenvalue sequence and is sorted from large to small; U = [μ1, μ2, …, μ N is the eigenvector corresponding to each eigenvalue; according to the maximum eigenvalue sequence in the interference detection step, first eliminate the eigenvalues containing interference pulses, then fit the eigenvalues without interference to obtain the dynamic threshold, and mark the a (1 < a < N) eigenvalues higher than the threshold as the eigenvalues corresponding to interference, and so on for the eigenvectors; expressed as L a =diag(λ1,λ2,…,λ a ) U a =[μ1,μ2,…,μ a ] The interference subspace is formed by the expansion of the eigenvector, expressed as span(U a ); Based on the Hankel matrix H m Perform interference subspace projection: WITH a =U a IN a H H m Interference subspace S a Contains all the information about mutual interference under high signal-to-interference-to-noise ratio conditions; Rearrange them to get a new vector, which is the interference reconstruction result under high signal-to-interference-noise ratio.

6. The method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering according to claim 1, characterized in that: The step S106 specifically includes the following steps: Based on the sparsity of echo signals, the mutual interference extraction problem can be transformed into an optimization problem of a semi-parametric method: under low signal-to-interference-noise ratio, the amplitude of the SAR target signal and the mutual interference signal differ greatly, so the overall SAR signal can be modeled as a low-rank smooth background, while the mutual interference completed by range compression can be modeled as a sparse target with abnormal energy; thus, sparse constraints are imposed on the mutual interference, and the semi-parametric optimization problem is solved; For the observed echo signal matrix S, ignoring the background noise, the above objective optimization problem can be expressed as Where λ represents a hyperparameter, ‖·‖0 is the L0 norm; since the target signal S r It has linear correlation and can be projected into a lower-dimensional subspace when the signal-to-noise ratio is low, expressed as a low-rank matrix. However, the matrix rank is non-convex, and it is difficult to perform the corresponding optimization solution, which extends to its convex approximate optimization solution, that is, the nuclear norm ‖·‖ * , the above optimization problem is updated to Where ‖·‖1 is the L1 norm, and its minimization is conducive to sparse constraints on mutual interference signals; By balancing the low-rank term and the sparse term by the weight parameter λ, when the optimization model meets the convergence condition or reaches the maximum number of iterations, the strong energy mutual interference can be effectively extracted under the condition of low signal-to-interference-noise ratio of the echo data.

7. The method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering according to claim 6, characterized in that: The step S107 specifically includes the following steps: Combine the reconstruction and extraction results of mutual interference under different signal-to-noise ratio data, arrange them according to the original pulse order, fill 0 with pulses without mutual interference, and obtain the final extracted mutual interference echo; extract the mutual interference from the interference-compressed echo signal S MF The mutual interference is eliminated, that is, the mutual interference is suppressed, which is expressed as Among them, M E represents the extracted mutual interference signal matrix, To remove the final signal matrix after mutual interference; at the same time, additional processing is required to restore the target signal affected by the matched filter, because the range compression processing of the mutual interference introduces unnecessary phases to the useful SAR signal; according to the design of the matched filter, the corresponding phase needs to be eliminated to express The distance spectrum of Among them, M F * (f) represents the matched filter M F The complex conjugate of (f) and then S r (f) Data recovery can be completed by performing inverse Fourier transform to obtain the final SAR signal after mutual interference suppression.

8. A spaceborne synthetic aperture radar scattering wave mutual interference suppression system based on matched filtering, characterized in that: include: An interference signal pulse compression unit, configured to estimate relevant parameters of interference and perform range-direction pulse compression on the interference signal in the echo data; An interference detection unit is configured to detect whether all range pulses contain interference using a maximum eigenvalue sequence detection method; A relative signal-to-interference ratio calculation unit, configured to calculate the relative signal-to-interference ratios of all interference-containing pulses by power comparison based on the interference detection result; The interference signal-to-interference-noise ratio classification unit is configured to classify the interference pulses according to the obtained relative signal-to-interference ratio to obtain two parts of data: high signal-to-interference-noise ratio and low signal-to-interference-noise ratio; An interference extraction unit, configured to reconstruct and extract interference signals under high signal-to-interference-noise ratio and low signal-to-interference-noise ratio conditions using a feature subspace projection method and a robust principal component analysis method respectively; The interference suppression and data recovery unit is configured to filter out the extracted interference signal from the echo data, and then perform mutual interference inverse range compression processing on the data to restore it to a normal echo without interference.

9. A storage medium storing a plurality of programs, characterized in that: The program application is loaded and executed by a processor to implement a method for suppressing mutual interference of spaceborne synthetic aperture radar scattered waves based on matched filtering as described in any one of claims 1-7.

10. An electronic device, characterized in that: It includes a storage medium and a processor; the processor is adapted to execute each program; The memory is used to store multiple programs; it is characterized in that when the memory executes the program on the processor, it implements a method for suppressing mutual interference of space-borne synthetic aperture radar scattered waves based on matched filtering as described in any one of claims 1-7.

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