Anti-interference radar signal extraction method and device and electronic equipment

Abstract

The invention discloses an anti-interference radar signal extraction method and device and electronic equipment, and the method comprises the steps: carrying out the whitening processing of a to-be-processed radar signal through a zero-phase component analysis whitening algorithm, and obtaining a to-be-separated radar signal after the whitening processing. An initial separation matrix is obtained by performing iterative optimization processing on a preset target separation function for multiple times. And performing fine adjustment on the initial separation matrix according to a second-order blind source separation algorithm to obtain a target separation matrix. Performing signal separation processing on the radar signal and the interference signal according to the target separation matrix to obtain an initial radar signal, and performing denoising processing on the initial radar signal according to a target denoising algorithm to obtain a radar signal, the target denoising algorithm being any one of a wavelet transform denoising algorithm and an improved stationary wavelet transform denoising algorithm. The problems of high calculation complexity and low efficiency in the prior art are avoided, and the stability and accuracy of radar signal extraction are improved.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to an anti-jamming radar signal extraction method, apparatus, and electronic device. Background Technology

[0002] In complex electromagnetic environments, radar signal processing faces severe challenges. Specifically, complex environments contain various interference sources, such as ocean clutter, ground clutter, electronic countermeasures signals, and noise signals. These interference sources severely disrupt radar signals, making it extremely difficult to extract target radar signals.

[0003] In existing technologies, on the one hand, traditional signal separation methods, such as those based on fast independent component analysis and wavelet thresholding, can separate radar signals and signals from various interference sources to obtain the target radar signal. On the other hand, deep learning-based signal separation methods can also separate radar signals and signals from various interference sources to obtain the target radar signal.

[0004] However, using existing technologies, traditional signal separation methods are not ideal for acquiring target radar signals in environments with strong noise and signal overlap. Deep learning-based signal separation methods suffer from high computational complexity and low efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide an anti-jamming radar signal extraction method, apparatus, and electronic device to solve the problems of unsatisfactory target radar signal acquisition, high computational complexity, and low efficiency in the prior art.

[0006] To achieve the above objectives, in a first aspect, embodiments of the present invention provide an anti-jamming radar signal extraction method, comprising: The radar signal to be processed is whitened using a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated. The radar signal to be separated includes the radar signal and the interference signal. An initial separation matrix is ​​obtained by iteratively optimizing the preset target separation function multiple times. The initial separation matrix is ​​fine-tuned using a second-order blind source separation algorithm to obtain the target separation matrix; The radar signal and the interference signal are separated according to the target separation matrix to obtain the initial radar signal; The initial radar signal is denoised according to the target denoising algorithm to obtain the radar signal, wherein the target denoising algorithm is either a wavelet transform denoising algorithm or an improved stationary wavelet transform denoising algorithm.

[0007] In one embodiment, the step of whitening the radar signal to be processed using a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated includes: The initial radar signal to be processed is subjected to zero-mean processing to obtain the radar signal to be processed; A whitening matrix is ​​constructed based on the feature vector matrix and eigenvalue diagonal matrix corresponding to the radar signal to be processed. The whitening matrix is ​​used to whiten the radar signal to be processed, and the whitened radar signal to be separated is obtained.

[0008] In one embodiment, obtaining the initial separation matrix by performing multiple iterative optimization processes on the preset target separation function includes: Construct a predefined target separation function; Multiple first separation matrices are randomized, and the multiple first separation matrices and the radar signal to be separated are respectively substituted into a preset target separation function for multiple iterative optimization processes to obtain multiple separation matrices; Obtain the standard value of the maximum correlation corresponding to each of the multiple separation matrices; The initial separation matrix is ​​determined by identifying the separation matrix corresponding to the largest maximum correlation standard value.

[0009] In one embodiment, the preset target separation function may be defined by the following expression:

[0010] in, Indicates the first i The first iteration of the optimization process k+ The separation matrix obtained by 1 iteration. Indicates the first i The first iteration of the optimization process t The radar signal to be separated at any given time. Indicates the first i The first iteration of the optimization process k The separation matrix obtained by the number of iterations. Indicates the first i The first iteration of the optimization process k The transpose of the separation matrix obtained from the number of iterations. Indicates about The values ​​of a family of symmetric nonlinear functions, Let the first mathematical expectation be represented by the product of the value of the symmetric family of nonlinear functions and the radar signal to be separated. Indicates about The symmetric family of nonlinear derivative values, This represents the second mathematical expectation corresponding to the value of the nonlinear derivative of a symmetric family. This represents the expected diagonal matrix of the second mathematical expectation.

[0011] In one embodiment, the step of fine-tuning the initial separation matrix according to the second-order blind source separation algorithm to obtain the target separation matrix includes: Based on the initial separation matrix and the radar signal to be separated, obtain the time delay covariance matrix; The target separation matrix is ​​obtained by minimizing the preset target optimization function based on the time delay covariance matrix.

[0012] In one embodiment, the preset target optimization function may be defined by the following expression:

[0013] in, Represents the eigenvector matrix, Indicates the sampling delay The time delay covariance matrix at that time, express The expected diagonal matrix, This represents the transpose of the eigenvector matrix. This represents the total sampling delay of the radar signal to be processed.

[0014] In one embodiment, before denoising the initial radar signal according to the target denoising algorithm and acquiring the radar signal, the method further includes: Based on the genetic algorithm, the target denoising algorithm is determined from wavelet transform denoising algorithm and improved stationary wavelet transform denoising algorithm.

[0015] In one embodiment, when the target denoising algorithm is determined to be an improved stationary wavelet transform denoising algorithm, the step of denoising the initial radar signal according to the target denoising algorithm to obtain the radar signal includes: The initial radar signal is decomposed into multiple layers to obtain the approximation coefficients and detail coefficients corresponding to each decomposition layer. The detail coefficients of multiple decomposition layers are substituted into a preset adaptive threshold function to dynamically determine the adaptive thresholds of multiple decomposition layers. Based on the approximation coefficients of multiple decomposition layers and the adaptive thresholds of multiple decomposition layers, the initial radar signal is subjected to inverse stationary wavelet transform denoising processing to obtain the intermediate radar signal. The intermediate radar signal is filtered using a Savitzky-Golay filter to obtain the radar signal.

[0016] Secondly, embodiments of the present invention provide an anti-jamming radar signal extraction device, comprising: The whitening module is used to whiten the radar signal to be processed by a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated. The radar signal to be separated includes radar signal and interference signal. The initial separation matrix acquisition module is used to obtain the initial separation matrix by performing multiple iterative optimization processes on the preset target separation function; The target separation matrix acquisition module is used to fine-tune the initial separation matrix according to the second-order blind source separation algorithm to obtain the target separation matrix; The initial radar signal acquisition module is used to perform signal separation processing on the radar signal and the interference signal according to the target separation matrix to acquire the initial radar signal; The radar signal acquisition module is used to perform denoising processing on the initial radar signal according to the target denoising algorithm to acquire the radar signal, wherein the target denoising algorithm is either a wavelet transform denoising algorithm or an improved stationary wavelet transform denoising algorithm.

[0017] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect.

[0018] The technical solution provided by the embodiments of the present invention has the following advantages compared with the prior art: This invention provides an anti-interference radar signal extraction method. It uses a zero-phase component analysis whitening algorithm to whiten the radar signal to be processed, obtaining a whitened radar signal to be separated. The radar signal to be separated includes both the radar signal and the interference signal. An initial separation matrix is ​​obtained by iteratively optimizing a preset target separation function. The initial separation matrix is ​​then fine-tuned using a second-order blind source separation algorithm to obtain a target separation matrix. The radar signal and interference signal are separated using the target separation matrix to obtain the initial radar signal. Finally, the initial radar signal is denoised using a target denoising algorithm, which can be either a wavelet transform denoising algorithm or an improved stationary wavelet transform denoising algorithm. This method, by combining whitening and optimizing the separation matrix using a second-order blind source separation algorithm, effectively improves the stability of radar signal separation while avoiding the high computational complexity and low efficiency problems of existing technologies. Furthermore, the target denoising algorithm further denoises the radar signal, effectively suppressing noise signals and improving the accuracy of radar signal acquisition. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart illustrating an anti-interference radar signal extraction method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an anti-interference radar signal extraction device provided in an embodiment of the present invention. Detailed Implementation

[0020] To facilitate a clear description of the technical solutions in the embodiments of the present invention, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0021] It should be noted that in this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0022] In this invention, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between the associated objects, indicating that three relationships can exist.

[0023] like Figure 1 As shown, Figure 1 This is a flowchart illustrating an anti-jamming radar signal extraction method provided in an embodiment of the present invention, which specifically includes the following steps: S10: The radar signal to be processed is whitened using a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated.

[0024] The Zero-phase Component Analysis Whitening (ZCA) algorithm is a linear transformation method that eliminates the correlation between data features and normalizes the variance of each dimension of the data to 1, preserving the spatial structure of the radar signal to be processed to the greatest extent. The radar signal to be separated includes the radar signal and the interference signal. The radar signal can be, for example, a linear frequency modulated signal, a binary frequency shift keying signal, a binary phase-coded signal, or a Costas frequency-coded signal. The interference signal can be, for example, 15 dB additive white Gaussian noise, sea clutter, noise amplitude-modulated interference, noise frequency-modulated interference, burst interference, or convolutional interference, but is not limited to these. This invention does not impose specific limitations, and those skilled in the art can set the appropriate settings according to the actual situation.

[0025] Specifically, after sampling the radar signal to be processed, the radar signal to be processed is whitened using a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated.

[0026] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S10 may be: S101: Perform zero-mean processing on the initial radar signal to be processed to obtain the radar signal to be processed.

[0027] Optionally, based on the above embodiments, in some embodiments of the present invention, according to the formula... The radar signal to be processed is subjected to zero-mean processing, wherein... This indicates the nth time after zero-mean processing. t The radar signals to be processed at any given time. Indicates the first t The initial radar signal to be processed at time 10:00. Indicates the first t The mathematical expectation of the initial radar signal to be processed at time t.

[0028] S102: Construct a whitening matrix based on the eigenvector matrix and eigenvalue diagonal matrix corresponding to the radar signal to be processed.

[0029] The whitening matrix can be defined by the following expression:

[0030] in, Represents the eigenvector matrix, Represents an eigenvalue diagonal matrix. This represents the transpose of the eigenvector matrix. This represents the whitening matrix.

[0031] S103: The radar signal to be processed is whitened by a whitening matrix to obtain the whitened radar signal to be separated.

[0032] Optionally, based on the above embodiments, in some embodiments of the present invention, according to the formula... The radar signal to be separated is determined, among which, Indicates the first t The radar signal to be separated at any given time. Represents the whitening matrix. Indicates the first t The radar signals to be processed at any given moment.

[0033] Thus, this embodiment eliminates constant offset by performing zero-mean processing on the initial radar signal to be processed. Furthermore, by performing whitening processing on the radar signal to be processed using a whitening matrix, the linear correlation between radar signals can be eliminated, and the spatial structure of the radar signal to be separated can be preserved to the greatest extent.

[0034] S11: Obtain the initial separation matrix by performing multiple iterative optimization processes on the preset target separation function.

[0035] The preset target separation function is used to obtain the separation matrix.

[0036] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S11 may be: S111: Construct a predefined target separation function.

[0037] The preset target separation function can be defined by the following expression:

[0038] in, Indicates the first i The first iteration of the optimization process k+ The separation matrix obtained by 1 iteration. Indicates the first i The first iteration of the optimization process t The radar signal to be separated at any given time. Indicates the first i The first iteration of the optimization process k The separation matrix obtained by the number of iterations. Indicates the first i The first iteration of the optimization process k The transpose of the separation matrix obtained from the number of iterations. Indicates about The values ​​of a family of symmetric nonlinear functions, Let the first mathematical expectation be represented by the product of the value of the symmetric family of nonlinear functions and the radar signal to be separated. Indicates about The symmetric family of nonlinear derivative values, This represents the second mathematical expectation corresponding to the value of the nonlinear derivative of a symmetric family. This represents the expected diagonal matrix of the second mathematical expectation.

[0039] S112: Randomize multiple first separation matrices, and substitute the multiple first separation matrices and the radar signal to be separated into the preset target separation function for multiple iterative optimization processes to obtain multiple separation matrices.

[0040] Specifically, multiple first separation matrices are obtained by randomization. Each first separation matrix and the radar signal to be separated are substituted into a preset target separation function. The preset target separation function is then iteratively optimized multiple times to obtain multiple separation matrices.

[0041] It should be noted that, in order to maintain the orthogonality of the separating vectors, the separating matrix obtained after each iteration must be whitened and normalized.

[0042] Specifically, according to the formula Whitening and normalization processes were performed, among which, Indicates the first i The first iteration of the optimization process k+ Transpose of the separation matrix obtained after 1 iteration.

[0043] S113: Obtain the maximum correlation standard value corresponding to each of the multiple separation matrices.

[0044] S114: Determine the separation matrix corresponding to the largest maximum correlation standard value as the initial separation matrix.

[0045] Specifically, after obtaining the separation matrices corresponding to the multiple first separation matrices, the maximum correlation standard value corresponding to the multiple separation matrices is obtained. The magnitudes of the multiple maximum correlation standard values ​​are compared, and the separation matrix corresponding to the largest maximum correlation standard value is determined as the initial separation matrix.

[0046] S12: Fine-tune the initial separation matrix according to the second-order blind source separation algorithm to obtain the target separation matrix.

[0047] Among them, the Second-Order Blind Source Separation (SOBI) algorithm is a blind source separation method based on the second-order statistical properties of signals, suitable for scenarios with unknown mixing matrices. It estimates the unmixing matrix by jointly diagonalizing the set of covariance matrices, thereby separating the source signals.

[0048] Optionally, based on the above embodiments, in some embodiments of the present invention, S12 may be implemented as follows: S121: Obtain the time delay covariance matrix based on the initial separation matrix and the radar signal to be separated.

[0049] Specifically, the radar signal to be separated is processed according to the initial separation matrix, and the processed radar signal to be separated is substituted into the preset time delay covariance matrix formula to obtain the time delay covariance matrix.

[0050] Optionally, based on the above embodiments, in some embodiments of the present invention, according to the formula... Determine the time delay covariance matrix, where, Indicates the sampling delay The time delay covariance matrix at that time, Indicates the first The signal obtained by processing the radar signal to be separated based on the initial separation matrix at any time. , Denotes the initial separation matrix. Indicates sampling delay. , This represents the total sampling delay of the radar signal to be processed. Indicates the first At time 1, the signal is the transpose of the radar signal to be separated after processing according to the initial separation matrix.

[0051] S122: Minimize the preset objective optimization function based on the time delay covariance matrix to obtain the objective separation matrix.

[0052] The preset target optimization function can be limited by the following expression:

[0053] in, Represents the eigenvector matrix, Indicates the sampling delay The time delay covariance matrix at that time, express The expected diagonal matrix, This represents the transpose of the eigenvector matrix. This represents the total sampling delay of the radar signal to be processed.

[0054] S13: Perform signal separation processing on the radar signal and the jamming signal according to the target separation matrix to obtain the initial radar signal.

[0055] Specifically, after obtaining the target separation matrix, the radar signal and the interference signal are separated using the target separation matrix to obtain the initial radar signal.

[0056] S14: Based on the target denoising algorithm, the initial radar signal is denoised to obtain the radar signal.

[0057] The target denoising algorithm is either the Discrete Wavelet Transform (DWT) or the Stationary Wavelet Transform (SWT) algorithm.

[0058] The above wavelet transform denoising algorithm processes the initial input radar signal through multiple cyclic shifts, denoises each shift independently, and then performs reverse shifting and averaging.

[0059] The improved stationary wavelet transform denoising algorithm described above achieves denoising processing of the initial radar signal by combining the stationary wavelet transform denoising algorithm with the Savitzky-Golay filter.

[0060] Specifically, after obtaining the initial radar signal, the initial radar signal is denoised using a wavelet transform denoising algorithm or an improved stationary wavelet transform denoising algorithm to obtain the radar signal.

[0061] Thus, the anti-interference radar signal extraction method provided in this embodiment whitens the radar signal to be processed using a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated, which includes both the radar signal and the interference signal. An initial separation matrix is ​​obtained by iteratively optimizing a preset target separation function multiple times. The initial separation matrix is ​​then fine-tuned using a second-order blind source separation algorithm to obtain the target separation matrix. The radar signal and interference signal are then separated using the target separation matrix to obtain the initial radar signal. Finally, the initial radar signal is denoised using a target denoising algorithm, which can be either a wavelet transform denoising algorithm or an improved stationary wavelet transform denoising algorithm. By combining whitening processing with optimization of the separation matrix based on a second-order blind source separation algorithm, the stability of radar signal separation processing is effectively improved, while avoiding the problems of high computational complexity and low efficiency in existing technologies. Furthermore, the target denoising algorithm further denoises the radar signal, effectively suppressing noise signals and further improving the accuracy of radar signal acquisition.

[0062] Optionally, based on the above embodiments, in some embodiments of the present invention, the method further includes the following before performing S14: S20: Based on the genetic algorithm, determine the target denoising algorithm from wavelet transform denoising algorithm and improved stationary wavelet transform denoising algorithm.

[0063] Optionally, based on the above embodiments, in some embodiments of the present invention, S20 may be specifically implemented as follows: S201: Obtain the denoising parameters of the wavelet transform denoising algorithm and the improved stationary wavelet transform denoising algorithm.

[0064] The denoising parameters include: number of decomposition layers, threshold adjustment factor, scaling factor, and wavelet basis index, which is used to determine the detail coefficients.

[0065] Among them, the threshold adjustment factor and scaling factor are used to adjust the adaptive threshold in the wavelet transform denoising algorithm and the improved stationary wavelet transform denoising algorithm, precisely control the hardness of the threshold, and thus perform continuous interpolation between hard and soft thresholds. This enables dynamic adjustment of denoising intensity and nonlinear compression to solve the problems of diversity and nonstationarity of the initial radar signal.

[0066] Optionally, based on the above embodiments, in some embodiments of the present invention, the adaptive threshold function may be defined by the following expression:

[0067] in, Indicates the threshold adjustment factor. Indicates the scaling factor. This represents the detail coefficients, which correspond to the wavelet basis index. The detail coefficients can be determined using the wavelet basis index. This represents the adaptive threshold during the denoising process.

[0068] It should be noted that the threshold adjustment strategies differ between wavelet transform denoising algorithms and improved stationary wavelet transform denoising algorithms. For wavelet transform denoising algorithms, an adaptive threshold is calculated for each decomposition layer, i.e., according to the formula... Determine the adaptive threshold for each decomposition layer, where, Indicates the first l The standard deviation of the detail coefficients of the decomposition layer Indicates the first l The number of detail coefficients in the decomposition layer.

[0069] The improved stationary wavelet transform denoising algorithm uses a single global threshold, calculating only one adaptive threshold for all decomposition layers, according to the formula... Determine the adaptive threshold, where, The standard deviation of noise Indicates total LThe total number of detail coefficients in the decomposition layer.

[0070] S202: Encode the denoising parameters as chromosomes, each chromosome being a four-dimensional real-valued vector. Through evolutionary search, within the first value range corresponding to the decomposition level, the second value range corresponding to the threshold adjustment factor, the third value range corresponding to the scaling factor, and the fourth value range corresponding to the wavelet basis index, determine the first optimal denoising parameters corresponding to the wavelet transform denoising algorithm based on the fitness function, and improve the second optimal denoising parameters corresponding to the stationary wavelet transform denoising algorithm.

[0071] The fitness function can be, for example, signal-to-noise ratio, normalized cross-correlation, root mean square error, and structural similarity index, but is not limited thereto. This invention does not impose specific limitations, and those skilled in the art can set it according to the actual situation.

[0072] The above applies to denoising parameters such as the number of decomposition layers. L Threshold adjustment factor Scaling factor and wavelet basis index Encode to obtain chromosomes .

[0073] The first value range corresponding to the aforementioned number of decomposition layers can be, for example, 6 to 10. By pre-setting the number of decomposition layers, the number of decomposition layers separating high-frequency and low-frequency information can be determined. A higher number of decomposition layers helps to analyze low-frequency trends in more detail, but it also increases the risk of noise aliasing during the decomposition process. Therefore, determining the optimal number of decomposition layers and achieving a balance between trend resolution and noise robustness is particularly important. Based on this, an adaptive optimization search algorithm is used to obtain the number of decomposition layers.

[0074] The second value range corresponding to the aforementioned threshold adjustment factor can be, for example, 0 to 0.6. By adjusting the hard and soft threshold shrinkage intensity of the detail coefficients of each decomposition layer, noise can be effectively suppressed. Specifically, if the threshold is too low, noise cannot be effectively suppressed; conversely, if the threshold is too high, valuable signal components will be lost. Based on this, an adaptive optimization is performed using a search algorithm to obtain the optimal threshold adjustment factor.

[0075] The third value range corresponding to the above scaling factor can be, for example, 0 to 4. The threshold value is finely adjusted by the energy characteristics of each wavelet decomposition layer. Based on this, the optimal scaling factor is obtained by adaptive optimization through a search algorithm.

[0076] The fourth value range corresponding to the aforementioned wavelet basis index can be, for example, code 1 to 20. Specifically, the wavelet basis index corresponding to different codes is determined by selecting from 20 predefined wavelet basis functions through encoding. These wavelet basis functions can be, for example, Haar wavelet, Daubechies wavelet, Symlets wavelet, etc. Different wavelet basis indices differ in terms of support length, vanishing moment, and edge response, which directly affect the sparsity and reconstruction accuracy of the signal. Based on this, an adaptive optimization is performed through a search algorithm to obtain the optimal wavelet basis index. Furthermore, the detail coefficients are determined based on the determined optimal wavelet basis index.

[0077] Optionally, based on the above embodiments, in some embodiments of the present invention, evolutionary search includes: selection search, crossover search, and mutation search.

[0078] In this process, a tournament selection method is used. Specifically, for each iteration, two individuals are randomly selected from the chromosome population. The fitness values ​​of the two individuals are obtained according to the fitness function, and the fitness values ​​are compared. The individual with the higher fitness value is selected to enter the next generation, thereby obtaining the optimal denoising parameters.

[0079] The cross-search employs a hybrid cross-search strategy, using different hybrid cross-search strategies for discrete chromosomes and continuous chromosomes, in order to obtain the optimal denoising parameters.

[0080] For example, the first parent chromosome is represented as: The second parent chromosome is represented as: .

[0081] The wavelet basis index and decomposition level of the discrete first and second parent chromosomes can be mixed and crossed as follows:

[0082] in, This represents the wavelet base index of the first parent chromosome that did not undergo mixing and crossing. This represents the wavelet basis index of the first parent chromosome after crossover. This represents the wavelet base index of the second parent chromosome that did not undergo mixing and crossing. This represents the wavelet basis index of the second parent chromosome after hybrid crossover. This indicates the number of decomposition layers of the first parent chromosome that did not undergo crossover. This indicates the number of layers of the first parent chromosome after crossover. This indicates the number of decomposition layers of the second parent chromosome that did not undergo crossover. This indicates the number of layers of the second parent chromosome after the crossover.

[0083] The threshold adjustment factor and scaling factor of the first and second parent chromosomes of continuous variables are mixed and crossed, which can be expressed as:

[0084] in, This represents a random number, with a value ranging from 0 to 1. The threshold adjustment factor represents the first parent chromosome that did not undergo crossover. This represents the threshold adjustment factor for the first parent chromosome after crossover. The threshold adjustment factor represents the second parent chromosome that did not undergo crossover. This represents the threshold adjustment factor for the second parent chromosome after crossover. The scaling factor representing the first parent chromosome that did not undergo crossover. This represents the scaling factor of the first parent chromosome after crossover. The scaling factor representing the second parent chromosome that did not undergo crossover. This represents the scaling factor of the second parent chromosome after the crossover.

[0085] Mutation search randomly selects individual chromosomes from the chromosome population and perturbs the components of these individual chromosomes with a mutation rate. Different mutation strategies are used for integer components and real-valued components to obtain the optimal denoising parameters.

[0086] For example, for integer components such as wavelet basis indices and decomposition levels, a mutation rate of 0.3 is set, and new wavelet basis indices and decomposition levels are randomly obtained from a preset range based on the mutation rate. For real-valued components such as threshold adjustment factors and scaling factors, Gaussian perturbations are added to the threshold adjustment factors and scaling factors with an independent probability of 0.5. Furthermore, the threshold adjustment factors and scaling factors with added Gaussian perturbations are projected onto a preset range to obtain new threshold adjustment factors and scaling factors.

[0087] S203: Based on the first fitness value corresponding to the first optimal denoising parameter and the second fitness value corresponding to the second optimal denoising parameter, determine the target denoising algorithm between the wavelet transform denoising algorithm and the improved stationary wavelet transform denoising algorithm.

[0088] Specifically, the first fitness value corresponding to the first optimal denoising parameter and the second fitness value corresponding to the second optimal denoising parameter are compared. If the first fitness value is greater than the second fitness value, the wavelet transform denoising algorithm is determined as the target denoising algorithm; otherwise, the improved stationary wavelet transform denoising algorithm is determined as the target denoising algorithm.

[0089] Optionally, based on the above embodiments, when the target denoising algorithm is determined to be an improved stationary wavelet transform denoising algorithm, in some embodiments of the present invention, another implementation of S14 may be: S141: Perform multi-layer decomposition processing on the initial radar signal to obtain the approximation coefficients and detail coefficients corresponding to each decomposition layer.

[0090] S142: Substitute the detail coefficients of multiple decomposition layers into the preset adaptive threshold function to dynamically determine the adaptive threshold of multiple decomposition layers.

[0091] S143: Based on the approximation coefficients of multiple decomposition layers and the adaptive thresholds of multiple decomposition layers, the initial radar signal is subjected to inverse stationary wavelet transform denoising processing to obtain the intermediate radar signal.

[0092] S144: The intermediate radar signal is filtered by the Savitzky-Golay filter to obtain the radar signal.

[0093] Specifically, after obtaining the initial radar signal, it is decomposed into multiple layers according to the number of decomposition layers to obtain the approximation coefficients and detail coefficients corresponding to each decomposition layer. After obtaining the detail coefficients of multiple decomposition layers, these coefficients are substituted into a preset adaptive threshold function to dynamically determine the adaptive thresholds for multiple decomposition layers. Based on the approximation coefficients and adaptive thresholds of multiple decomposition layers, the initial radar signal is subjected to inverse stationary wavelet transform denoising to obtain the intermediate radar signal. Finally, the intermediate radar signal is filtered using a Savitzky-Golay filter to obtain the final radar signal.

[0094] Optionally, based on the above embodiments, in some embodiments of the present invention, according to the formula... Identify the radar signal.

[0095] in, This represents the radar signal after denoising the initial radar signal. Indicates use A polynomial of order 1 with a sliding window length of 1 Savitzky-Golay filter, Indicates the total number of decomposition levels. Indicates the total decomposition layer Approximation coefficients, Indicates the first The detail factor of the decomposition layer, Indicates the first The adaptive threshold function values ​​corresponding to the detail coefficients of the decomposition layer. This indicates inverse stationary wavelet transform for denoising.

[0096] Optionally, based on the above embodiments, when the target denoising algorithm is determined to be a wavelet transform denoising algorithm, in some embodiments of the present invention, one implementation of S14 may be: According to the formula Acquire radar signals.

[0097] in, This represents the total number of sampling points in the cyclic shift. Indicates the total number of decomposition levels. Indicates the total number of decomposition levels Approximation coefficients, Indicates the first The detail coefficients of the decomposition layer, Indicates the first The adaptive threshold function values ​​corresponding to the detail coefficients of the decomposition layer. This indicates inverse wavelet transform denoising. The first radar signal represents the initial radar signal. One sampling point, Indicates the initial radar signal Perform a cyclic reverse shift operation on each sampling point.

[0098] In one embodiment, such as Figure 2 As shown, Figure 2 The schematic diagram of an anti-interference radar signal extraction device provided in an embodiment of the present invention includes: a whitening processing module 10, an initial separation matrix acquisition module 11, a target separation matrix acquisition module 12, an initial radar signal acquisition module 13, and a radar signal acquisition module 14.

[0099] The whitening processing module 10 is used to whiten the radar signal to be processed by a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated. The radar signal to be separated includes the radar signal and the interference signal.

[0100] The initial separation matrix acquisition module 11 is used to obtain the initial separation matrix by performing multiple iterative optimization processes on the preset target separation function.

[0101] The target separation matrix acquisition module 12 is used to fine-tune the initial separation matrix according to the second-order blind source separation algorithm to obtain the target separation matrix.

[0102] The initial radar signal acquisition module 13 is used to perform signal separation processing on the radar signal and the interference signal according to the target separation matrix to acquire the initial radar signal.

[0103] The radar signal acquisition module 14 is used to denoise the initial radar signal according to the target denoising algorithm to acquire the radar signal. The target denoising algorithm is either the wavelet transform denoising algorithm or the improved stationary wavelet transform denoising algorithm.

[0104] In the above embodiments, the initial separation matrix acquisition module obtains the initial separation matrix by performing multiple iterative optimization processes on a preset target separation function. The target separation matrix acquisition module then fine-tunes the initial separation matrix using a second-order blind source separation algorithm to obtain the target separation matrix. The initial radar signal acquisition module performs signal separation processing on the radar signal and interference signal based on the target separation matrix to obtain the initial radar signal. The radar signal acquisition module then performs denoising processing on the initial radar signal using a target denoising algorithm to obtain the radar signal. The target denoising algorithm can be either a wavelet transform denoising algorithm or an improved stationary wavelet transform denoising algorithm. Thus, by combining whitening processing and optimizing the separation matrix based on a second-order blind source separation algorithm, the stability of radar signal separation processing is effectively improved, while avoiding the problems of high computational complexity and low efficiency in existing technologies. Furthermore, using a target denoising algorithm to further denoise the radar signal effectively suppresses noise signals, further improving the accuracy of radar signal acquisition.

[0105] Specific limitations regarding the anti-jamming radar signal extraction device can be found in the limitations of the anti-jamming radar signal extraction method described above, and will not be repeated here. Each module in the aforementioned server can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0106] This invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can implement the anti-jamming radar signal extraction method provided in this invention. For example, when the processor executes the computer program, it can implement... Figure 1 The technical solutions of any of the method embodiments shown are similar in implementation principle and technical effect, and will not be described again here.

[0107] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM), etc.

[0108] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed invention. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0109] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely exemplary descriptions of the invention as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for extracting anti-jamming radar signals, characterized in that, include: The radar signal to be processed is whitened using a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated. The radar signal to be separated includes the radar signal and the interference signal. An initial separation matrix is ​​obtained by iteratively optimizing the preset target separation function multiple times. The initial separation matrix is ​​fine-tuned using a second-order blind source separation algorithm to obtain the target separation matrix; The radar signal and the interference signal are separated according to the target separation matrix to obtain the initial radar signal; The initial radar signal is denoised according to the target denoising algorithm to obtain the radar signal, wherein the target denoising algorithm is either a wavelet transform denoising algorithm or an improved stationary wavelet transform denoising algorithm.

2. The method according to claim 1, characterized in that, The step of using a zero-phase component analysis whitening algorithm to whiten the radar signal to be processed and obtaining the whitened radar signal to be separated includes: The initial radar signal to be processed is subjected to zero-mean processing to obtain the radar signal to be processed; A whitening matrix is ​​constructed based on the feature vector matrix and eigenvalue diagonal matrix corresponding to the radar signal to be processed. The whitening matrix is ​​used to whiten the radar signal to be processed, and the whitened radar signal to be separated is obtained.

3. The method according to claim 2, characterized in that, The process of obtaining an initial separation matrix by performing multiple iterative optimizations on a preset target separation function includes: Construct a predefined target separation function; Multiple first separation matrices are randomized, and the multiple first separation matrices and the radar signal to be separated are respectively substituted into a preset target separation function for multiple iterative optimization processes to obtain multiple separation matrices; Obtain the standard value of the maximum correlation corresponding to each of the multiple separation matrices; The initial separation matrix is ​​determined by identifying the separation matrix corresponding to the largest maximum correlation standard value.

4. The method according to claim 3, characterized in that, The preset target separation function can be defined by the following expression: ； in, Indicates the first i The first iteration of the optimization process k+ The separation matrix obtained by 1 iteration. Indicates the first i The first iteration of the optimization process t The radar signal to be separated at any given time. Indicates the first i The first iteration of the optimization process k The separation matrix obtained by the number of iterations. Indicates the first i The first iteration of the optimization process k The transpose of the separation matrix obtained from the number of iterations. Indicates about The values ​​of a family of symmetric nonlinear functions, Let the first mathematical expectation be represented by the product of the value of the symmetric family of nonlinear functions and the radar signal to be separated. Indicates about The symmetric family of nonlinear derivative values, This represents the second mathematical expectation corresponding to the value of the nonlinear derivative of a symmetric family. This represents the expected diagonal matrix of the second mathematical expectation.

5. The method according to claim 4, characterized in that, The step of fine-tuning the initial separation matrix according to the second-order blind source separation algorithm to obtain the target separation matrix includes: Based on the initial separation matrix and the radar signal to be separated, obtain the time delay covariance matrix; The target separation matrix is ​​obtained by minimizing the preset target optimization function based on the time delay covariance matrix.

6. The method according to claim 5, characterized in that, The preset target optimization function can be defined by the following expression: ； in, Represents the eigenvector matrix, Indicates the sampling delay The time delay covariance matrix at that time, express The expected diagonal matrix, This represents the transpose of the eigenvector matrix. This represents the total sampling delay of the radar signal to be processed.

7. The method according to claim 1, characterized in that, Before acquiring the radar signal by denoising the initial radar signal according to the target denoising algorithm, the method further includes: Based on the genetic algorithm, the target denoising algorithm is determined from wavelet transform denoising algorithm and improved stationary wavelet transform denoising algorithm.

8. The method according to claim 7, characterized in that, When the target denoising algorithm is determined to be an improved stationary wavelet transform denoising algorithm, the step of denoising the initial radar signal according to the target denoising algorithm to obtain the radar signal includes: The initial radar signal is decomposed into multiple layers to obtain the approximation coefficients and detail coefficients corresponding to each decomposition layer. The detail coefficients of multiple decomposition layers are substituted into a preset adaptive threshold function to dynamically determine the adaptive thresholds of multiple decomposition layers. Based on the approximation coefficients of multiple decomposition layers and the adaptive thresholds of multiple decomposition layers, the initial radar signal is subjected to inverse stationary wavelet transform denoising processing to obtain the intermediate radar signal. The intermediate radar signal is filtered using a Savitzky-Golay filter to obtain the radar signal.

9. An anti-interference radar signal extraction device, characterized in that, include: The whitening module is used to whiten the radar signal to be processed by a zero-phase component analysis whitening algorithm to obtain the whitened radar signal to be separated. The radar signal to be separated includes radar signal and interference signal. The initial separation matrix acquisition module is used to obtain the initial separation matrix by performing multiple iterative optimization processes on the preset target separation function; The target separation matrix acquisition module is used to fine-tune the initial separation matrix according to the second-order blind source separation algorithm to obtain the target separation matrix; The initial radar signal acquisition module is used to perform signal separation processing on the radar signal and the interference signal according to the target separation matrix to acquire the initial radar signal; The radar signal acquisition module is used to perform denoising processing on the initial radar signal according to the target denoising algorithm to acquire the radar signal, wherein the target denoising algorithm is either a wavelet transform denoising algorithm or an improved stationary wavelet transform denoising algorithm.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the anti-jamming radar signal extraction method according to any one of claims 1 to 8.

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