SAR (Synthetic Aperture Radar) anti-interference evaluation method and device based on maximum variance combined with hierarchical entropy weight

Abstract

The invention provides an SAR (Synthetic Aperture Radar) anti-interference evaluation method and device based on maximum variance combined with layered entropy weight, and relates to the technical field of radar anti-interference. Comprising the following steps: constructing an evaluation index matrix according to a plurality of target SAR echoes; dividing the plurality of evaluation indexes into two layers of indexes by using a Pearson correlation analysis algorithm according to the evaluation index matrix and a preset threshold value; according to the plurality of evaluation indexes, the random number and the normalized evaluation index matrix, constructing a maximum variance evaluation optimization model which takes a maximized weighted evaluation matrix and a utility value matrix as objective functions and takes a difference degree weight row vector as a parameter to be solved; according to the maximum variance evaluation optimization model and the evaluation index matrix, determining a maximum difference evaluation matrix by using an interior point method; and according to the maximum difference evaluation matrix, the evaluation index matrix and the two layers of indexes, determining a plurality of evaluation values corresponding to the plurality of anti-interference methods by using a layered entropy weight method. In this way, the accuracy of anti-interference evaluation of various anti-interference methods is high.

Description

Technical Field

[0001] This invention relates to the field of radar anti-jamming technology, and in particular to a method and apparatus for evaluating the anti-jamming capabilities of Synthetic Aperture Radar (SAR) using maximum variance joint hierarchical entropy weighting. Background Technology

[0002] With increasingly complex electromagnetic interference environments, various anti-interference methods are emerging. Evaluating the effectiveness of anti-interference methods is a crucial comprehensive assessment indicator for evaluating the effectiveness of these methods and the anti-interference performance of SAR systems. A reasonable anti-interference effectiveness evaluation method can distinguish the advantages and disadvantages of different anti-interference methods, thus providing a basis for selecting anti-interference methods under different interference scenarios.

[0003] Currently, traditional SAR anti-jamming effectiveness evaluation methods are used to assess the performance of different interference suppression algorithms. However, the evaluation indicators selected by these traditional methods are often only effective for evaluating anti-jamming effectiveness under a single type of interference. When combined interference exists, the selected indicators cannot reasonably evaluate the effectiveness of different anti-jamming methods, resulting in low accuracy in evaluating anti-jamming effectiveness for multiple methods. Summary of the Invention

[0004] The purpose of this invention is to provide a SAR anti-interference assessment method and apparatus based on maximum variance combined with hierarchical entropy weights, thereby solving the problem of low accuracy in anti-interference assessment for various anti-interference methods.

[0005] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a SAR anti-interference assessment method based on maximum variance joint hierarchical entropy weights, comprising: An evaluation index matrix is ​​constructed based on SAR echoes from multiple targets. The evaluation index matrix is ​​a matrix containing multiple evaluation indexes, and the SAR echoes from multiple targets are echoes processed using various anti-interference methods. Based on the evaluation index matrix and preset thresholds, the Pearson correlation analysis algorithm is used to divide multiple evaluation indicators into two levels of indicators. The evaluation index matrix is ​​normalized to obtain a normalized evaluation index matrix. Based on multiple evaluation indicators, random numbers, and a normalized evaluation indicator matrix, a maximum variance evaluation optimization model is constructed. The maximum variance evaluation optimization model is an optimization model with the objective function of maximizing the weighted evaluation matrix and the utility value matrix, and the differential weight row vector as the parameter to be solved. Based on the maximum variance evaluation optimization model and evaluation index matrix, the maximum variance evaluation matrix is ​​determined using the interior point method. Based on the maximum difference evaluation matrix, the evaluation index matrix, and the two-level index, the hierarchical entropy weight method is used to determine multiple evaluation values ​​corresponding to various anti-interference methods. These multiple evaluation values ​​are used to indicate the anti-interference effect of various anti-interference methods.

[0006] A second aspect of the present invention provides a SAR anti-interference evaluation device based on maximum variance joint hierarchical entropy weights, comprising: The first construction module is used to construct an evaluation index matrix based on multiple target SAR echoes. The evaluation index matrix is ​​a matrix containing multiple evaluation indexes, and the multiple target SAR echoes are echoes processed using various anti-interference methods. The partitioning module is used to divide multiple evaluation indicators into two levels of indicators based on the evaluation indicator matrix and preset thresholds using the Pearson correlation analysis algorithm. The normalization module is used to normalize the evaluation index matrix to obtain a normalized evaluation index matrix. The second construction module is used to construct a maximum variance evaluation optimization model based on multiple evaluation indicators, random numbers, and a normalized evaluation indicator matrix. The maximum variance evaluation optimization model is an optimization model with the objective function of maximizing the weighted evaluation matrix and the utility value matrix, and the difference weight row vector as the parameter to be solved. The first determination module is used to determine the maximum variance evaluation matrix based on the maximum variance evaluation optimization model and evaluation index matrix, using the interior point method. The second determination module is used to determine multiple evaluation values ​​corresponding to various anti-interference methods based on the maximum difference evaluation matrix, the evaluation index matrix, and the two-layer index, using the hierarchical entropy weight method. These multiple evaluation values ​​are used to indicate the anti-interference effect of various anti-interference methods.

[0007] Compared to existing technologies, the SAR anti-interference assessment method and apparatus based on maximum variance combined with hierarchical entropy weighting provided by this invention constructs an assessment index matrix based on multiple target SAR echoes; based on the assessment index matrix and preset thresholds, the Pearson correlation analysis algorithm is used to divide the multiple assessment indexes into two layers of indexes; the assessment index matrix is ​​normalized to obtain a normalized assessment index matrix; based on multiple assessment indexes, random numbers, and the normalized assessment index matrix, a maximum variance assessment optimization model is constructed, which is an optimization model with the objective function of maximizing the weighted assessment matrix and the utility value matrix, and the difference weight row vector as the parameter to be solved; based on the maximum variance assessment optimization model and the assessment index matrix, the interior point method is used to determine the maximum difference assessment matrix; based on the maximum difference assessment matrix, the assessment index matrix, and the two layers of indexes, the hierarchical entropy weighting method is used to determine multiple evaluation values ​​corresponding to various anti-interference methods. In this way, the constructed maximum variance evaluation optimization model can maximize the weighted evaluation matrix and utility value matrix, enhancing the discriminative effect of different anti-interference methods. Furthermore, by using the interior point method to solve the maximum variance evaluation optimization model, the differences between multiple evaluation values ​​corresponding to different anti-interference methods are amplified, making it easier to evaluate the performance of different anti-interference methods. The maximum variance evaluation optimization model, combined with the hierarchical entropy weight method, can be easily extended to multiple evaluation indicators, resulting in high accuracy in anti-interference evaluation of various anti-interference methods. Attached Figure Description

[0008] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein: Figure 1 A flowchart illustrating the SAR anti-interference assessment method with maximum variance joint hierarchical entropy weights is shown. Figure 2 A schematic diagram illustrating the absolute values ​​of the Pearson correlation coefficient matrix is ​​shown. Figure 3 A schematic diagram illustrating the division of indicators into two levels is shown. Figure 4 This diagram illustrates the relationship between the number of iterations and the function value when solving the maximum variance evaluation optimization model using the interior point method. Figure 5 A schematic diagram of the SAR anti-interference assessment device with maximum variance joint hierarchical entropy weight is shown. Detailed Implementation

[0009] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0010] It should be noted that, unless otherwise stated, the technical or scientific terms used in this invention should have the ordinary meaning as understood by those skilled in the art.

[0011] The methods in the embodiments of the present invention will be described in detail below.

[0012] Figure 1 The flowchart of the SAR anti-interference evaluation method with maximum variance joint hierarchical entropy weights in an embodiment of the present invention is illustrated schematically. See [link to flowchart illustration]. Figure 1 As shown, the SAR anti-interference assessment method based on maximum variance joint hierarchical entropy weights can include: S101. Construct an evaluation index matrix based on SAR echoes from multiple targets.

[0013] The evaluation index matrix is ​​a matrix containing multiple evaluation indexes, and the multiple target SAR echoes are echoes processed using various anti-jamming methods. The multiple evaluation indexes include image entropy, equivalent number of views, dynamic range, interference energy suppression ratio, signal distortion, and image sharpness.

[0014] Multiple anti-interference methods can provide The various anti-interference methods can include notch filtering, subspace projection filtering, and robust principal component analysis (RPCA) decomposition, etc. There are no restrictions on the various anti-interference methods here, as long as they are existing anti-interference methods.

[0015] Prior to step S101, the method further includes: acquiring multiple target SAR echoes processed using various anti-interference methods.

[0016] In the embodiments of the present invention, six evaluation indicators for anti-interference and imaging can be selected from multiple evaluation indicators: image entropy, equivalent number of views, dynamic range, interference energy suppression ratio, signal distortion, and image sharpness. Among these, smaller values ​​for image entropy, equivalent number of views, and signal distortion indicate better anti-interference performance; therefore, image entropy, equivalent number of views, and signal distortion in the evaluation indicator matrix are defined as negative indicators. Conversely, larger values ​​for dynamic range, interference energy suppression ratio, and image sharpness indicate better anti-interference performance; therefore, dynamic range, interference energy suppression ratio, and image sharpness in the evaluation indicator matrix are defined as positive indicators.

[0017] Calculated based on SAR echoes from multiple targets Six evaluation indicators were used to construct an evaluation indicator matrix for the various anti-interference methods. : ; in, To evaluate the indicator matrix, For the first The first anti-interference method corresponds to the first One evaluation indicator, The value can be 1~ ,correspond Index of anti-interference methods The value ranges from 1 to 6, representing the indexes of six evaluation indicators in sequence: image entropy, equivalent number of views, dynamic range, interference energy suppression ratio, signal distortion, and image sharpness. This is the first evaluation index corresponding to the first anti-interference method. This is the sixth evaluation index corresponding to the first anti-interference method. This is the first evaluation index corresponding to the second anti-interference method. This is the sixth evaluation index corresponding to the second anti-interference method. For the first The first evaluation index corresponding to the anti-interference method, For the first The sixth evaluation index corresponding to the anti-interference method.

[0018] S102. Based on the evaluation index matrix and preset thresholds, the Pearson correlation analysis algorithm is used to divide the multiple evaluation indicators into two-level indicators.

[0019] Specifically, based on the evaluation index matrix and preset thresholds, the Pearson correlation analysis algorithm is used to divide multiple evaluation indicators into two levels of indicators, including: Step A1: Perform Pearson correlation analysis on the evaluation index matrix using the Pearson correlation analysis algorithm to construct the Pearson correlation coefficient matrix.

[0020] Select any two column vectors corresponding to any two evaluation indicators in the evaluation indicator matrix. and To determine the Pearson correlation coefficient between two column vectors, the expression for the Pearson correlation coefficient between two column vectors is: ; in, This represents the Pearson correlation coefficient, specifically the absolute value of the Pearson correlation coefficient. Values , The larger the value, the stronger the correlation. For the evaluation index matrix, the first The column vector of a column. For the evaluation index matrix, the first The column vector of a column. For the evaluation index matrix, the first The mean of the column vectors of a column. For the evaluation index matrix, the first The mean of the column vectors of a column. For the evaluation index matrix, the first The corresponding column Vectors of various anti-interference methods For the evaluation index matrix, the first The corresponding column Vectors for various anti-interference methods.

[0021] A Pearson correlation coefficient matrix was constructed by calculating the pairwise Pearson correlation coefficients of the six evaluation indicators (i.e., the six columns of evaluation indicators). Figure 2 A schematic diagram illustrating the absolute values ​​of the Pearson correlation coefficient matrix is ​​shown below. Figure 2 As shown, the Pearson correlation coefficient matrix is ​​symmetrically distributed along its diagonal axis. The row vectors of the Pearson correlation coefficient matrix represent, in order, image entropy, equivalent number of views, dynamic range, interference energy suppression ratio, signal distortion, and image sharpness. The column vectors also represent, in order, image entropy, equivalent number of views, dynamic range, interference energy suppression ratio, signal distortion, and image sharpness. The value in each cell of the Pearson correlation coefficient matrix represents the absolute value of the correlation coefficient between the corresponding row and column vectors. For example, the value in the cell corresponding to the row vector in row 6 and the column vector in column 1 is 0.04954. 0.04954 represents the Pearson correlation coefficient between image entropy and image sharpness.

[0022] Step A2: Based on the Pearson correlation coefficient matrix and preset thresholds, divide the multiple evaluation indicators into two levels of indicators.

[0023] The preset threshold can be 0.7. If the absolute values ​​of multiple first Pearson correlation coefficients in the Pearson correlation coefficient matrix are higher than the preset threshold of 0.7, then the evaluation indicators corresponding to the absolute values ​​of the multiple first Pearson correlation coefficients are classified as first-level indicators. If the absolute values ​​of multiple second Pearson correlation coefficients in the Pearson correlation coefficient matrix are lower than the preset threshold of 0.7, then the evaluation indicators corresponding to the absolute values ​​of the multiple second Pearson correlation coefficients are classified as second-level indicators. For example, Figure 3 A schematic diagram illustrating the division of indicators into two levels is shown below. Figure 2 and Figure 3 As shown, Figure 2For the three evaluation metrics—image entropy, equivalent number of views, and dynamic range—the absolute values ​​of the Pearson correlation coefficients are higher than 0.7, classifying image entropy, equivalent number of views, and dynamic range as first-level metrics. For the three evaluation metrics—interference energy suppression ratio, signal distortion, and image sharpness—the absolute values ​​of the Pearson correlation coefficients are lower than 0.7, classifying interference energy suppression ratio, signal distortion, and image sharpness as second-level metrics.

[0024] S103. Normalize the evaluation index matrix to obtain the normalized evaluation index matrix.

[0025] The positive index values ​​(i.e., dynamic range, interference energy suppression ratio, and image sharpness) divided in step S101 are normalized according to the following first normalization expression: ; in, For the first The normalized first anti-interference method corresponds to the second method. The column vector corresponding to each evaluation indicator For the first The first anti-interference method corresponds to the first The column vector corresponding to each evaluation indicator For the first The column vectors corresponding to the evaluation metrics are: dynamic range, interference energy suppression ratio, and image sharpness. , For the first The evaluation metrics are dynamic range, interference energy suppression ratio, and image sharpness. This represents the total number of anti-interference methods. For the first The corresponding evaluation indicator is the first The column vector of the anti-interference method To find the function with the maximum value, To find the minimum value of the function.

[0026] The negative index values ​​(i.e., image entropy, equivalent number of views, and signal distortion) obtained in step S101 are normalized according to the following second normalization expression: ; in, For the first The normalized first anti-interference method corresponds to the second method. The column vector corresponding to each evaluation indicator For the first The first anti-interference method corresponds to the first The column vector corresponding to each evaluation indicator For the first The column vectors corresponding to the evaluation metrics are: image entropy, equivalent number of views, and signal distortion. , For the first The evaluation metrics are image entropy, equivalent number of views, and signal distortion. For the first The corresponding evaluation indicator is the first A column vector of anti-interference methods.

[0027] The first The normalized first anti-interference method corresponds to the second method. The column vector corresponding to each evaluation indicator , and the The normalized first anti-interference method corresponds to the second method. The column vector corresponding to each evaluation indicator According to the evaluation index matrix The various anti-interference methods are arranged in order to obtain a normalized evaluation index matrix. .

[0028] The expression for the normalized evaluation index matrix is: ; in, For the normalized evaluation index matrix, For the first The first anti-interference method corresponds to the first Normalized evaluation indicators for each evaluation indicator. This is the normalized evaluation index for the first evaluation index corresponding to the first anti-interference method. This is the normalized evaluation index for the sixth evaluation index corresponding to the first anti-interference method. This is the normalized evaluation index for the first evaluation index corresponding to the second anti-interference method. This is the normalized evaluation index for the sixth evaluation index corresponding to the second anti-interference method. For the first The normalized evaluation index for the first evaluation index corresponding to the anti-interference method. For the first The normalized evaluation index for the sixth evaluation index corresponding to the anti-interference method.

[0029] S104. Based on multiple evaluation indicators, random numbers, and a normalized evaluation indicator matrix, construct a maximum variance evaluation optimization model.

[0030] Among them, the maximum variance evaluation optimization model is an optimization model with the objective function of maximizing the weighted evaluation matrix and the utility value matrix, and the difference weight row vector as the parameter to be solved.

[0031] Specifically, based on multiple evaluation metrics, random numbers, and a normalized evaluation metric matrix, a maximum variance evaluation optimization model is constructed, including: Step B1: Determine the multiple preset weights to be solved corresponding to multiple evaluation indicators as preset difference weight row vectors, and initialize the preset difference weight row vectors with random numbers to obtain the difference weight row vectors.

[0032] Will Let represent the preset weights to be solved for each of the six evaluation metrics: image entropy, equivalent number of views, dynamic range, interference energy suppression ratio, signal distortion, and image sharpness. The expression for the preset difference weight row vector is defined as follows: ,in, For the preset difference weight row vector, For the first The preset weights to be solved for each evaluation indicator The first evaluation metric, i.e., image entropy, corresponds to the preset weights to be solved. The second evaluation index, namely the equivalent number of views, corresponds to the preset weights to be solved. The preset weights to be solved are the sixth evaluation index, namely image sharpness.

[0033] Initialize the preset difference weight row vector with random numbers The difference weight row vector is obtained. That is, the initialized difference weight row vector.

[0034] The expression for the dissimilarity weight row vector is: ,in, For the difference weight row vector, For the first The weights to be solved for each evaluation index The weight to be solved is the first evaluation metric, i.e., image entropy. The weight to be solved is the equivalent apparent number, which is the second evaluation index. The weight to be solved is the sixth evaluation index, namely image sharpness.

[0035] Step B2: Using the difference weight row vector, perform weighted calculations on each row element in the normalized evaluation index matrix to obtain a multi-row weighted evaluation index vector. Then, sort the multi-row weighted evaluation index vectors according to the target arrangement order to obtain the weighted evaluation matrix.

[0036] The target order is the order of various anti-interference methods in the normalized evaluation index matrix.

[0037] The expression for the weighted evaluation index vector is as follows: ; in, This is the weighted vector of evaluation indicators after multiple rows, i.e., the first row. The multi-row weighted evaluation index vector corresponding to the various anti-interference methods For the first The row elements of the normalized evaluation index matrix corresponding to the anti-interference method, , For the first The first row element in the normalized evaluation index matrix corresponding to the anti-interference method, For the first The sixth row element in the normalized evaluation index matrix corresponding to the anti-interference method For the difference weight row vector, This is a dot product operation.

[0038] The weighted evaluation matrix is ,in, For weighted evaluation matrix, These are the row elements in the weighted evaluation matrix corresponding to the first anti-interference method. For the first Row elements in the weighted evaluation matrix corresponding to each anti-interference method.

[0039] Step B3: Based on the difference weight row vector and the multi-row weighted evaluation index vector, determine the multi-row utility values ​​corresponding to various anti-interference methods, and sort the multi-row utility values ​​according to the target arrangement order to obtain the utility value matrix.

[0040] The expression for multi-line utility values ​​is: ; in, For the first The utility value corresponding to each anti-interference method For the difference weight row vector, For the first The multi-row weighted evaluation index vector corresponding to the various anti-interference methods For the first The transpose of the multi-row weighted evaluation index vector corresponding to the anti-interference method It is the vector inner product.

[0041] The utility value matrix is ,in, For utility value matrix, This represents the utility value corresponding to the first anti-interference method. For the first The utility value corresponding to each anti-interference method.

[0042] Step B4: Using the maximization of the weighted evaluation matrix and the utility value matrix as the objective function, and the difference weight row vector as the parameter to be solved, construct the maximum variance evaluation optimization model.

[0043] The expression for the maximum variance evaluation optimization model is: ; in, For the matrix variance, For weighted evaluation matrix, For utility value matrix, To calculate the variance of the weighted evaluation matrix, The variance of the utility value matrix. For the difference weight row vector, For the first One evaluation indicator, For the first The weights to be solved for each evaluation indicator.

[0044] S105. Based on the maximum variance evaluation optimization model and evaluation index matrix, determine the maximum variance evaluation matrix using the interior point method.

[0045] Based on the maximum variance evaluation optimization model and evaluation index matrix, the maximum variance evaluation matrix is ​​determined using the interior point method, including: Step C1: Solve the maximum variance evaluation optimization model using the interior point method to obtain the final evaluation matrix difference weight row vector.

[0046] Specifically, step C1 includes: Step C11: Based on the current penalty factor and multiple weights to be solved, augment the maximum variance evaluation optimization model using the interior point method to obtain the current penalty function.

[0047] Specifically, the expression for the current penalty function is: ; in, The current penalty function is based on the difference weight row vector and the current penalty factor. For the difference weight row vector, As the current penalty factor, , For the matrix variance, For weighted evaluation matrix, For utility value matrix, To calculate the variance of the weighted evaluation matrix, The variance of the utility value matrix. For the first One evaluation indicator, For the first The weights to be solved for each evaluation indicator.

[0048] Step C12: Set the initial difference weight row vector to a preset vector.

[0049] The preset vector is Set the initial difference weight row vector to the expression of the preset vector: ,in, This is the initial row vector of dissimilarity weights. Let be the initial weight to be solved for the first evaluation index corresponding to the initial difference weight row vector. This represents the initial weight to be solved for the 6th evaluation index corresponding to the initial difference weight row vector.

[0050] Step C13: Set the current gradient of the current penalty function to the preset gradient value to obtain the expression of the current gradient. Based on the expression of the current gradient, solve for the corresponding current difference weight row vector.

[0051] The preset gradient value is 0. The expression that sets the current gradient of the current penalty function to the preset gradient value is: ,in, This represents the current gradient of the current penalty function. Solve for the corresponding current difference weight row vector. ,in, This is the current row vector of difference weights, i.e., the row vector of difference weights in the first iteration. This represents the current weight to be solved for the first evaluation index corresponding to the current row vector of difference weights. This represents the current weight to be solved for the 6th evaluation index corresponding to the current difference weight row vector.

[0052] Step C14: Determine the initial value of the current penalty function based on the preset vector, and determine the current value of the current penalty function based on the current difference weight row vector.

[0053] Step C15: Determine that the difference between the current value and the initial value is not less than the error threshold.

[0054] The expression for the difference between the current value and the initial value, i.e., the iteration precision, is: ,in, This represents the current value of the current penalty function. This is the initial value of the current penalty function. For the error threshold, .

[0055] If the difference between the current value and the initial value is not less than the error threshold, proceed to step C16.

[0056] Step C16: Based on the next penalty factor and multiple weights to be solved, the maximum variance evaluation optimization model is augmented using the interior point method to obtain the next penalty function.

[0057] The next penalty factor is a penalty factor that is a preset multiple of the current penalty factor.

[0058] The preset multiplier is 0.1. ,in, As the next penalty factor, This is the current penalty factor. The expression for the next penalty function is to apply the penalty factor from the expression of the current penalty function. Replace with .

[0059] Step C17: Set the next gradient of the next penalty function to the preset gradient value to obtain the expression of the next gradient. Based on the expression of the next gradient, solve for the corresponding next difference weight row vector.

[0060] Step C18: Determine the next value of the next penalty function based on the next difference weight row vector.

[0061] Step C19: Determine whether the difference between the next value and the current value is less than the error threshold. If yes, determine the next difference weight row vector as the final evaluation matrix difference weight row vector. If no, take another penalty factor as the next penalty factor and return the step of augmenting the maximum variance evaluation optimization model based on the next penalty factor and multiple weights to be solved using the interior point method to obtain the next penalty function. Continue until the difference between another value and the next value is less than the error threshold, then stop the iteration operation and determine the difference weight row vector corresponding to the next value as the final evaluation matrix difference weight row vector.

[0062] Specifically, determine whether the difference between the next value and the current value is less than the error threshold. If yes, determine the next difference weight row vector as the final evaluation matrix difference weight row vector. If no, take another penalty factor as the next penalty factor and return to step C16. Repeat steps C16 to C19 until the difference between another value and the next value is less than the error threshold. Stop the iteration operation and determine the difference weight row vector corresponding to the next value as the final evaluation matrix difference weight row vector.

[0063] The expression for the difference between another value and the next value is: ,in, Let be yet another value of the penalty function, that is, the th... Another value of the penalty function corresponding to the next iteration. The next value of the next penalty function, i.e., the [number]th [value]. The next value of the penalty function corresponding to the next iteration. This is another row vector of difference weights, namely the first row vector. The row vector of difference weights in the next iteration , For the first Another weight to be solved for the first evaluation index corresponding to the difference weight row vector in the next iteration. For the first Another weight to be solved for the 6th evaluation index corresponding to the difference weight row vector in the next iteration. For the next row vector of difference weights, i.e., the first row... The row vector of difference weights in the next iteration , For the first The next weight to be solved for the first evaluation index corresponding to the row vector of difference weights in the next iteration. For the first The next weight to be solved for the sixth evaluation index corresponding to the difference weight row vector of the next iteration.

[0064] The final evaluation matrix difference weight row vector is represented as follows: ,in, To finally evaluate the matrix difference weight row vector, The final weight to be solved is the weight of the first evaluation index corresponding to the row vector of the final evaluation matrix difference weight. This is the final weight to be solved for the 6th evaluation index corresponding to the row vector of the final evaluation matrix difference weight.

[0065] Figure 4 The diagram illustrates the relationship between the number of iterations and the function value when solving the maximum variance evaluation optimization model using the interior point method. (See attached diagram.) Figure 4 As shown, the horizontal axis represents the number of iterations, and the vertical axis represents the function value of the penalty function. After 25 iterations, the function value of the penalty function basically stabilizes. The corresponding iteration precision, i.e., the function value of the penalty function corresponding to the 25th iteration, differs from the function value of the penalty function corresponding to the 24th iteration by 0.0000012, which is less than the error threshold. In other words, the expression for the difference between the function value of the penalty function corresponding to the 25th iteration and the function value of the penalty function corresponding to the 24th iteration is: ; in, This is the function value of the penalty function corresponding to the 25th iteration. This is the function value of the penalty function corresponding to the 24th iteration. At this point, the solution... corresponding ,in, This represents the gradient of the penalty function corresponding to the 25th iteration. This is the row vector of difference weights in the 25th iteration. The weight to be solved for the first evaluation index corresponding to the row vector of difference weights in the 25th iteration is... This represents the weight to be solved for the 6th evaluation index corresponding to the difference weight row vector in the 25th iteration. The difference weight row vector from the 25th iteration is used as the difference weight row vector in the final evaluation matrix. .

[0066] Step C2: Using the row vector of the difference weight of the final evaluation matrix, perform weighted calculation on the evaluation index matrix to obtain the evaluation matrix with the maximum difference.

[0067] The expression for the maximum dissimilarity assessment matrix is: ; in, This is the maximum difference assessment matrix. To evaluate the indicator matrix, This is the row vector for the final evaluation matrix difference weights.

[0068] By using the row vector of the difference weight in the final evaluation matrix to perform weighted calculations on the evaluation index matrix, the difference of each element in the evaluation index matrix can be amplified.

[0069] S106. Based on the maximum difference evaluation matrix, the evaluation index matrix, and the two-level index, the hierarchical entropy weight method is used to determine multiple evaluation values ​​corresponding to various anti-interference methods.

[0070] Among them, multiple evaluation values ​​are used to indicate the anti-interference effect of various anti-interference methods.

[0071] Based on the maximum difference evaluation matrix, the evaluation index matrix, and the two-layer index, the hierarchical entropy weight method is used to determine multiple evaluation values ​​corresponding to various anti-interference methods, including: Step D1: Based on the maximum difference evaluation matrix and the two-level indicators, use the hierarchical entropy weight method to determine the final evaluation weight corresponding to the maximum difference evaluation matrix.

[0072] The maximum difference assessment matrix includes multiple maximum difference assessment indicators, including target dynamic range, target interference energy suppression ratio, target image sharpness, target image entropy, target equivalent number of views, and target signal distortion. Target dynamic range, target interference energy suppression ratio, and target image sharpness are classified as positive target indicator values, while target image entropy, target equivalent number of views, and target signal distortion are classified as negative target indicator values.

[0073] Specifically, step D1 includes: Step D11: Normalize the target dynamic range, target interference energy suppression ratio, and target image sharpness in the maximum difference evaluation matrix according to the first normalization expression in step S103. Normalize the target image entropy, target equivalent number of views, and target signal distortion in the maximum difference evaluation matrix according to the second normalization expression in step S103 to obtain the normalized maximum difference evaluation matrix. .

[0074] Step D12: Based on the two-layer indicators in step S102, the multiple maximum difference evaluation indicators of the normalized maximum difference evaluation matrix are divided into first-layer target indicators and second-layer target indicators. The first-layer target indicators include target image entropy, target equivalent number of views and target dynamic range. The second-layer target indicators include target interference energy suppression ratio, target signal distortion and target image sharpness.

[0075] Step D13: Select the target column vectors corresponding to the target image entropy, target equivalent number of views and target dynamic range in the normalized maximum difference evaluation matrix to form the first normalized evaluation matrix, and construct the first second-level weight vector to be solved corresponding to the first-level target index.

[0076] The first normalized evaluation matrix is ​​represented as: ,in, This is the first normalized evaluation matrix. The target column vector is the entropy of the target image. Let the target column vector be the equivalent number of views of the target. This is the target column vector corresponding to the target's dynamic range.

[0077] The first second-level weight vector to be solved is represented as follows: ,in, The first second-level weight vector to be solved. The second-order weights to be solved are the entropy of the target image. The second-order weights to be solved are the equivalent apparent numbers of the target. The second-level weights to be solved are the dynamic range of the target.

[0078] Step D14: Select the target interference energy suppression ratio, target signal distortion and target image sharpness from the normalized maximum difference evaluation matrix to form the second normalized evaluation matrix, and construct the second unsolved secondary weight vector corresponding to the second layer target index.

[0079] The second normalized evaluation matrix is ​​expressed as: ,in, This is the second normalized evaluation matrix. Let the target column vector be the target interference energy suppression ratio. This represents the target column vector corresponding to the target signal distortion. This is the target column vector corresponding to the sharpness of the target image.

[0080] The second-level weight vector to be solved is represented as follows: ,in, The first second-order weight to be solved. The second-order weights to be solved are the target interference energy suppression ratio. The second-level weights to be solved are the target signal distortion. The second-level weights to be solved are the target image sharpness.

[0081] Step D15: Calculate the first normalized evaluation matrix. The first anti-interference method The maximum difference evaluation index, which accounts for the largest proportion among all maximum difference evaluation indexes corresponding to anti-interference methods. .

[0082] The expression for the first proportion is: ; in, As the first proportion, The first normalized evaluation matrix is ​​the first... The first anti-interference method The largest difference assessment index, This is the maximum difference evaluation index corresponding to all anti-interference methods.

[0083] Step D16: Calculate the first normalized evaluation matrix according to the first weight. The first information entropy of the maximum difference evaluation index .

[0084] The expression for the first information entropy is: ; in, The first normalized evaluation matrix is ​​the first... The first information entropy of the maximum difference assessment index This represents the total number of anti-interference methods. It accounts for the largest proportion.

[0085] Step D17: Based on the first information entropy, calculate the first normalized evaluation matrix. The first and second-level weights of the maximum difference assessment indicators This is to solve for the first second-level weight vector to obtain the first second-level weight vector.

[0086] The expressions for the first and second level weights are: ; in, The first normalized evaluation matrix is ​​the first... The first and second-level weights of the maximum difference assessment indicators The first normalized evaluation matrix is ​​the first... The first information entropy of the maximum difference assessment index.

[0087] The first and second level weight vectors are represented as follows: ,in, These are the first and second level weight vectors. This refers to the first and second-level weights of the first largest difference evaluation index in the first normalized evaluation matrix, which are the first and second-level weights corresponding to the target image entropy. This refers to the first and second-level weights of the second largest difference evaluation index in the first normalized evaluation matrix, i.e., the first and second-level weights corresponding to the target equivalent apparent number. This refers to the first and second-level weights of the third largest difference evaluation index in the first normalized evaluation matrix, which are the first and second-level weights corresponding to the target dynamic range.

[0088] Similarly, based on the expression for the proportion in step D15, substitute the second normalized evaluation matrix into it to calculate the first proportion in the second normalized evaluation matrix. The first anti-interference method The second largest difference evaluation index, representing the second proportion among all the largest difference evaluation indices corresponding to all anti-interference methods. Based on this second proportion, the second normalized evaluation matrix is ​​calculated. The second information entropy of the maximum difference evaluation index is calculated based on the second information entropy. The second and second-level weights of the maximum difference assessment indicators This is to solve for the second secondary weight vector to obtain the second secondary weight vector.

[0089] The second-level weight vector is represented as follows: ,in, This is the second-level weight vector. This refers to the second-level weight of the fourth largest difference evaluation index in the second normalized evaluation matrix, which is the second-level weight corresponding to the target image entropy. This refers to the second-level weight of the fifth largest difference assessment indicator in the second normalized assessment matrix, which is the second-level weight corresponding to the target equivalent apparent number. This is the second-level weight of the sixth largest difference evaluation index in the second normalized evaluation matrix, which is the second-level weight corresponding to the target dynamic range.

[0090] Step D18: Based on the first and second-level weight vectors, perform weighted calculation on the first normalized evaluation matrix to obtain the first-level evaluation matrix corresponding to the first normalized evaluation matrix. Based on the second and second-level weight vectors, perform weighted calculation on the second normalized evaluation matrix to obtain the first-level evaluation matrix corresponding to the second normalized evaluation matrix.

[0091] The expressions for the first-level evaluation matrix corresponding to the first normalized evaluation matrix and the first-level evaluation matrix corresponding to the second normalized evaluation matrix are as follows: ; ; in, This is the first-level evaluation matrix corresponding to the first normalized evaluation matrix. This is the first normalized evaluation matrix. These are the first and second level weight vectors. This is the first-level evaluation matrix corresponding to the second normalized evaluation matrix. This is the second normalized evaluation matrix. This is the second-level weight vector.

[0092] Step D19: Calculate the first final evaluation weight in the final evaluation weight based on the first-level evaluation matrix and the first and second-level weights corresponding to the first normalized evaluation matrix, and calculate the second final evaluation weight in the final evaluation weight based on the first-level evaluation matrix and the second and second-level weights corresponding to the second normalized evaluation matrix.

[0093] Specifically, calculate the weight of the first objective in the first-level evaluation matrix corresponding to the first normalized evaluation matrix and the first-level evaluation matrix corresponding to the second normalized evaluation matrix, and calculate the weight of the second objective in the first-level evaluation matrix corresponding to the second normalized evaluation matrix and the first-level evaluation matrix corresponding to the second normalized evaluation matrix. ; ; in, The first objective weighting, i.e., the variability of the first-level evaluation matrix corresponding to the first normalized evaluation matrix, The weighting of the second objective is the variability of the first-level evaluation matrix corresponding to the second normalized evaluation matrix. This is the first-level evaluation matrix corresponding to the first normalized evaluation matrix. This is the first-level evaluation matrix corresponding to the second normalized evaluation matrix.

[0094] Based on the weight of the first objective, calculate the information entropy of the first-level evaluation matrix corresponding to the first normalized evaluation matrix, and based on the weight of the second objective, calculate the information entropy of the first-level evaluation matrix corresponding to the second normalized evaluation matrix. ; ; in, The information entropy of the first-level evaluation matrix corresponding to the first normalized evaluation matrix. The information entropy of the first-level evaluation matrix corresponding to the second normalized evaluation matrix. The primary target proportion, The proportion of the second objective.

[0095] Based on the information entropy of the first-level evaluation matrix corresponding to the first normalized evaluation matrix, calculate the weights of the first-level indicators corresponding to the first normalized evaluation matrix. Similarly, based on the information entropy of the first-level evaluation matrix corresponding to the second normalized evaluation matrix, calculate the weights of the first-level indicators corresponding to the second normalized evaluation matrix. ; ; in, The weights of the first-level indicators corresponding to the first normalized evaluation matrix are: The weights of the first-level indicators corresponding to the second normalized evaluation matrix. The information entropy of the first-level evaluation matrix corresponding to the first normalized evaluation matrix. The information entropy of the first-level evaluation matrix corresponding to the second normalized evaluation matrix.

[0096] Based on the primary indicator weights and primary and secondary weights corresponding to the first normalized evaluation matrix, the first final evaluation weight in the final evaluation weight is calculated, and based on the primary indicator weights and secondary weights corresponding to the second normalized evaluation matrix, the second final evaluation weight in the final evaluation weight is calculated, so as to obtain the final evaluation weight.

[0097] The expressions for the first and second final evaluation weights are: ; ; in, As the first final evaluation weight, As the first and second level weights, As the second final evaluation weight, As the second and second level weights, The weights of the first-level indicators corresponding to the first normalized evaluation matrix are: The weights of the first-level indicators corresponding to the second normalized evaluation matrix.

[0098] The final evaluation weights are expressed as follows: ,in, For the final evaluation weight, This is the first final evaluation weight of the first maximum difference evaluation index in the first normalized evaluation matrix, i.e., the first final evaluation weight of the target image entropy. This is the first final evaluation weight of the second largest difference evaluation index in the first normalized evaluation matrix, i.e., the first final evaluation weight of the target equivalent apparent number. This is the second final evaluation weight of the sixth largest difference evaluation index in the second normalized evaluation matrix, namely the second final evaluation weight of the target image sharpness weight.

[0099] Step D2: Using the final evaluation weights, perform a weighted summation of the evaluation index matrix to obtain multiple evaluation values ​​corresponding to various anti-interference methods.

[0100] The expressions for the multiple evaluation values ​​corresponding to the various anti-interference methods are as follows: ; in, These are multiple evaluation values ​​corresponding to various anti-interference methods. To evaluate the indicator matrix, For the final evaluation weight, This is the evaluation value corresponding to the first anti-interference method. For the first Evaluation values ​​corresponding to various anti-interference methods.

[0101] The higher the evaluation value, the better the anti-interference imaging effect.

[0102] After obtaining multiple evaluation values, these values ​​can be arranged in descending order. The top-ranked evaluation value is selected, and the anti-interference method corresponding to this top-ranked value is obtained. SAR echo data is then processed using this method. Because the anti-interference method corresponding to the highest evaluation value is selected for processing the SAR echo data, a better anti-interference effect can be achieved.

[0103] This invention uses the Pearson correlation analysis algorithm to stratify different anti-interference evaluation indicators, achieving a more objective and accurate stratification effect, thereby obtaining a more accurate final evaluation weight. By acquiring online anti-interference evaluation indicators, constructing a maximum variance optimization model, and combining it with the stratified entropy weight method to obtain the final weight, the invention can efficiently and accurately complete the dynamic online evaluation of anti-interference effect.

[0104] Based on the above Figure 1As can be seen from the implementation method, the embodiments of the present invention construct an evaluation index matrix based on multiple target SAR echoes; based on the evaluation index matrix and preset thresholds, the Pearson correlation analysis algorithm is used to divide the multiple evaluation indexes into two layers of indexes; the evaluation index matrix is ​​normalized to obtain a normalized evaluation index matrix; based on multiple evaluation indexes, random numbers, and the normalized evaluation index matrix, a maximum variance evaluation optimization model is constructed. The maximum variance evaluation optimization model is an optimization model with maximizing the weighted evaluation matrix and utility value matrix as objective functions and the difference weight row vector as the parameter to be solved; based on the maximum variance evaluation optimization model and the evaluation index matrix, the interior point method is used to determine the maximum difference evaluation matrix; based on the maximum difference evaluation matrix, the evaluation index matrix, and the two layers of indexes, the hierarchical entropy weight method is used to determine multiple evaluation values ​​corresponding to various anti-interference methods. In this way, the constructed maximum variance evaluation optimization model can maximize the weighted evaluation matrix and utility value matrix, enhancing the discriminative effect of different anti-interference methods. Furthermore, by using the interior point method to solve the maximum variance evaluation optimization model, the differences between multiple evaluation values ​​corresponding to different anti-interference methods are amplified, making it easier to evaluate the performance of different anti-interference methods. The maximum variance evaluation optimization model, combined with the hierarchical entropy weight method, can be easily extended to multiple evaluation indicators, resulting in high accuracy in anti-interference evaluation of various anti-interference methods.

[0105] Based on the same inventive concept, as an implementation of the above-mentioned SAR anti-interference evaluation method with maximum variance joint hierarchical entropy weight, this embodiment of the invention also provides a SAR anti-interference evaluation device with maximum variance joint hierarchical entropy weight. Figure 5 This is a structural diagram of the SAR anti-interference evaluation device with maximum variance joint hierarchical entropy weights in an embodiment of the present invention. See also... Figure 5 As shown, the SAR anti-interference assessment device with maximum variance joint hierarchical entropy weights may include: The first construction module 501 is used to construct an evaluation index matrix based on multiple target SAR echoes. The evaluation index matrix is ​​a matrix containing multiple evaluation indexes, and the multiple target SAR echoes are echoes processed using multiple anti-interference methods. The partitioning module 502 is used to divide multiple evaluation indicators into two levels of indicators based on the evaluation indicator matrix and preset thresholds using the Pearson correlation analysis algorithm. The normalization processing module 503 is used to normalize the evaluation index matrix to obtain a normalized evaluation index matrix. The second construction module 504 is used to construct a maximum variance evaluation optimization model based on multiple evaluation indicators, random numbers and normalized evaluation indicator matrices. The maximum variance evaluation optimization model is an optimization model with the objective function of maximizing the weighted evaluation matrix and utility value matrix, and the differential weight row vector as the parameter to be solved. The first determining module 505 is used to determine the maximum variance evaluation matrix based on the maximum variance evaluation optimization model and the evaluation index matrix using the interior point method. The second determining module 506 is used to determine multiple evaluation values ​​corresponding to various anti-interference methods based on the maximum difference evaluation matrix, the evaluation index matrix and the two-layer index, using the hierarchical entropy weight method. The multiple evaluation values ​​are used to indicate the anti-interference effect of various anti-interference methods.

[0106] The partitioning module 502 is specifically used to perform Pearson correlation analysis on the evaluation index matrix using the Pearson correlation analysis algorithm to construct the Pearson correlation coefficient matrix; based on the Pearson correlation coefficient matrix and preset thresholds, multiple evaluation indicators are divided into two-level indicators.

[0107] The first determining module 505 is specifically used to solve the maximum variance evaluation optimization model using the interior point method to obtain the final evaluation matrix difference weight row vector; and to use the final evaluation matrix difference weight row vector to perform weighted calculation on the evaluation index matrix to obtain the maximum difference evaluation matrix.

[0108] The second determining module 506 is specifically used to determine the final evaluation weight corresponding to the maximum difference evaluation matrix based on the maximum difference evaluation matrix and the two-layer indicators, using the hierarchical entropy weight method; and to use the final evaluation weight to perform weighted summation on the evaluation indicator matrix to obtain multiple evaluation values ​​corresponding to various anti-interference methods.

[0109] The second construction module 504 is specifically used to determine multiple preset weights to be solved for multiple evaluation indicators as preset difference weight row vectors, and initialize the preset difference weight row vectors with random numbers to obtain difference weight row vectors; using the difference weight row vectors, perform weighted calculations on each row element in the normalized evaluation indicator matrix to obtain multi-row weighted evaluation indicator vectors, and sort the multi-row weighted evaluation indicator vectors according to the target arrangement order to obtain a weighted evaluation matrix, where the target arrangement order is the arrangement order of multiple anti-interference methods in the normalized evaluation indicator matrix; based on the difference weight row vectors and the multi-row weighted evaluation indicator vectors, determine the multi-row utility values ​​corresponding to multiple anti-interference methods, and sort the multi-row utility values ​​according to the target arrangement order to obtain a utility value matrix; using the maximization of the weighted evaluation matrix and the utility value matrix as the objective function, and using the difference weight row vectors as the parameters to be solved, construct a maximum variance evaluation optimization model.

[0110] In the second building module 504, the expression for the maximum variance evaluation optimization model is: ; in, For the matrix variance, For weighted evaluation matrix, For utility value matrix, To calculate the variance of the weighted evaluation matrix, The variance of the utility value matrix. For the difference weight row vector, For the first One evaluation indicator, For the first The weights to be solved for each evaluation indicator.

[0111] The first determining module 505 uses the interior-point method to solve the maximum variance evaluation optimization model to obtain the final evaluation matrix difference weight row vector. This includes: augmenting the maximum variance evaluation optimization model using the interior-point method based on the current penalty factor and multiple weights to be solved, obtaining the current penalty function; setting the initial difference weight row vector as a preset vector; setting the current gradient of the current penalty function as a preset gradient value, obtaining the expression for the current gradient, and solving the corresponding current difference weight row vector based on the expression for the current gradient; determining the initial value of the current penalty function based on the preset vector, and determining the current value of the current penalty function based on the current difference weight row vector; determining that the difference between the current value and the initial value is not less than an error threshold; and augmenting the maximum variance evaluation optimization model using the interior-point method based on the next penalty factor and multiple weights to be solved, obtaining the next penalty function. The penalty factor is a penalty factor that is a preset multiple of the current penalty factor; the next gradient of the next penalty function is set to a preset gradient value to obtain the expression of the next gradient; based on the expression of the next gradient, the corresponding next difference weight row vector is solved; the next value of the next penalty function is determined based on the next difference weight row vector; it is determined whether the difference between the next value and the current value is less than the error threshold. If so, the next difference weight row vector is determined as the difference weight row vector of the final evaluation matrix; if not, another penalty factor is used as the next penalty factor, and the steps of augmenting the maximum variance evaluation optimization model using the interior point method based on the next penalty factor and multiple weights to be solved to obtain the next penalty function are returned. The iteration operation stops when the difference between another value and the next value is less than the error threshold, and the difference weight row vector corresponding to the other value is determined as the difference weight row vector of the final evaluation matrix.

[0112] In the first determination module 505, the expression of the current penalty function is: ; in, The penalty function is based on the difference weight row vector and the current penalty factor. For the difference weight row vector, As the current penalty factor, For the matrix variance, For weighted evaluation matrix, For utility value matrix, To calculate the variance of the weighted evaluation matrix, The variance of the utility value matrix. For the first One evaluation indicator, For the first The weights to be solved for each evaluation indicator.

[0113] It should be noted that the above description of the SAR anti-interference assessment device embodiment with maximum variance joint hierarchical entropy weight is similar to the description of the SAR anti-interference assessment method embodiment with maximum variance joint hierarchical entropy weight, and has similar beneficial effects. For technical details not disclosed in the embodiments of the SAR anti-interference assessment device with maximum variance joint hierarchical entropy weight of the present invention, please refer to the description of the SAR anti-interference assessment method embodiment with maximum variance joint hierarchical entropy weight of the present invention for understanding.

[0114] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A SAR anti-interference assessment method based on maximum variance joint hierarchical entropy weights, characterized in that, include: An evaluation index matrix is ​​constructed based on multiple target SAR echoes. The evaluation index matrix is ​​a matrix containing multiple evaluation indexes, and the multiple target SAR echoes are echoes processed using various anti-interference methods. Based on the evaluation index matrix and preset thresholds, the multiple evaluation indicators are divided into two levels of indicators using the Pearson correlation analysis algorithm. The evaluation index matrix is ​​normalized to obtain a normalized evaluation index matrix. Based on the multiple evaluation indicators, random numbers, and the normalized evaluation indicator matrix, a maximum variance evaluation optimization model is constructed. The maximum variance evaluation optimization model is an optimization model with the objective function of maximizing the weighted evaluation matrix and the utility value matrix, and the difference weight row vector as the parameter to be solved. Based on the maximum variance evaluation optimization model and the evaluation index matrix, the maximum variance evaluation matrix is ​​determined using the interior point method. Based on the maximum difference evaluation matrix, the evaluation index matrix, and the two-layer index, the hierarchical entropy weight method is used to determine multiple evaluation values ​​corresponding to the various anti-interference methods. These multiple evaluation values ​​are used to indicate the anti-interference effect of the various anti-interference methods.

2. The SAR anti-interference assessment method based on maximum variance joint hierarchical entropy weights according to claim 1, characterized in that, The evaluation indexes are divided into two levels based on the evaluation index matrix and preset thresholds using the Pearson correlation analysis algorithm, including: For the evaluation index matrix, Pearson correlation analysis is performed using the Pearson correlation analysis algorithm to construct the Pearson correlation coefficient matrix; Based on the Pearson correlation coefficient matrix and the preset threshold, the multiple evaluation indicators are divided into the two-layer indicators.

3. The SAR anti-interference assessment method based on maximum variance joint hierarchical entropy weights according to claim 1, characterized in that, The step of determining the maximum variance evaluation matrix using the interior point method based on the maximum variance evaluation optimization model and the evaluation index matrix includes: The maximum variance evaluation optimization model is solved using the interior point method to obtain the final evaluation matrix difference weight row vector. The evaluation index matrix is ​​weighted using the row vector of the difference weight of the final evaluation matrix to obtain the maximum difference evaluation matrix.

4. The SAR anti-interference assessment method based on maximum variance joint hierarchical entropy weights according to claim 1, characterized in that, The step involves determining multiple evaluation values ​​corresponding to the various anti-interference methods using the hierarchical entropy weight method, based on the maximum difference evaluation matrix, the evaluation index matrix, and the two-layer indexes. These values ​​include: Based on the maximum difference evaluation matrix and the two-layer indicators, the final evaluation weight corresponding to the maximum difference evaluation matrix is ​​determined using the hierarchical entropy weight method. Using the final evaluation weights, the evaluation index matrix is ​​weighted and summed to obtain multiple evaluation values ​​corresponding to the various anti-interference methods.

5. The SAR anti-interference evaluation method based on maximum variance joint hierarchical entropy weights according to claim 3, characterized in that, The step of constructing a maximum variance evaluation optimization model based on the multiple evaluation indicators, random numbers, and the normalized evaluation indicator matrix includes: The multiple preset weights to be solved corresponding to the multiple evaluation indicators are determined as preset difference weight row vectors, and the preset difference weight row vectors are initialized with the random numbers to obtain the difference weight row vectors. Using the difference weight row vector, the elements of each row in the normalized evaluation index matrix are weighted to obtain a multi-row weighted evaluation index vector. The multi-row weighted evaluation index vector is then sorted according to the target arrangement order to obtain the weighted evaluation matrix. The target arrangement order is the arrangement order of multiple anti-interference methods in the normalized evaluation index matrix. Based on the difference weight row vector and the multi-row weighted evaluation index vector, the multi-row utility values ​​corresponding to the various anti-interference methods are determined, and the multi-row utility values ​​are sorted according to the target arrangement order to obtain the utility value matrix. The maximum variance evaluation optimization model is constructed by using the maximization of the weighted evaluation matrix and the utility value matrix as the objective function and the difference weight row vector as the parameter to be solved.

6. The SAR anti-interference evaluation method based on maximum variance joint hierarchical entropy weights according to claim 5, characterized in that, The difference weight row vector includes multiple weights to be solved corresponding to the multiple evaluation indicators, and the expression of the maximum variance evaluation optimization model is: ； in, For the matrix variance, The weighted evaluation matrix is... The utility value matrix is... The variance of the weighted evaluation matrix is... The variance of the utility value matrix is... Let the difference weight row vector be... For the first One evaluation indicator, For the first The weights to be solved for each evaluation indicator.

7. The SAR anti-interference evaluation method based on maximum variance joint hierarchical entropy weights according to claim 6, characterized in that, The step of solving the maximum variance evaluation optimization model using the interior point method to obtain the final evaluation matrix difference weight row vector includes: Based on the current penalty factor and the multiple weights to be solved, the maximum variance evaluation optimization model is augmented using the interior point method to obtain the current penalty function. Set the initial difference weight row vector to a preset vector; Set the current gradient of the current penalty function to a preset gradient value to obtain the expression of the current gradient. Based on the expression of the current gradient, solve for the corresponding current difference weight row vector. The initial value of the current penalty function is determined based on the preset vector, and the current value of the current penalty function is determined based on the current difference weight row vector; Determine that the difference between the current value and the initial value is not less than the error threshold; Based on the next penalty factor and the multiple weights to be solved, the maximum variance evaluation optimization model is augmented using the interior point method to obtain the next penalty function, wherein the next penalty factor is a penalty factor that is a preset multiple of the current penalty factor; Set the next gradient of the next penalty function to the preset gradient value to obtain the expression of the next gradient. Based on the expression of the next gradient, solve for the corresponding next difference weight row vector. The next value of the next penalty function is determined based on the next difference weight row vector; If the difference between the next value and the current value is less than the error threshold, then the next difference weight row vector is determined as the difference weight row vector of the final evaluation matrix. If not, another penalty factor is used as the next penalty factor, and the process of augmenting the maximum variance evaluation optimization model using the interior point method based on the next penalty factor and the multiple weights to be solved is returned to obtain the next penalty function. This process continues until the difference between another value and the next value is less than the error threshold, at which point the iteration stops, and the difference weight row vector corresponding to the next value is determined as the difference weight row vector of the final evaluation matrix.

8. The SAR anti-interference evaluation method based on maximum variance joint hierarchical entropy weights according to claim 7, characterized in that, The expression for the current penalty function is: ； in, The penalty function is based on the difference weight row vector and the current penalty factor. Let the difference weight row vector be... The current penalty factor, For the matrix variance, The weighted evaluation matrix is... The utility value matrix is... The variance of the weighted evaluation matrix is... The variance of the utility value matrix is... For the first One evaluation indicator, For the first The weights to be solved for each evaluation indicator.

9. The SAR anti-interference evaluation method based on maximum variance joint hierarchical entropy weights according to claim 4, characterized in that, The evaluation metrics include image entropy, equivalent number of views, dynamic range, interference energy suppression ratio, signal distortion, and image sharpness.

10. A SAR anti-interference evaluation device based on maximum variance joint hierarchical entropy weights, characterized in that, include: The first construction module is used to construct an evaluation index matrix based on multiple target SAR echoes. The evaluation index matrix is ​​a matrix containing multiple evaluation indexes, and the multiple target SAR echoes are echoes processed using various anti-interference methods. The partitioning module is used to divide the multiple evaluation indicators into two levels of indicators based on the evaluation indicator matrix and preset thresholds using the Pearson correlation analysis algorithm. The normalization processing module is used to normalize the evaluation index matrix to obtain a normalized evaluation index matrix. The second construction module is used to construct a maximum variance evaluation optimization model based on the multiple evaluation indicators, random numbers and the normalized evaluation indicator matrix. The maximum variance evaluation optimization model is an optimization model with the objective function of maximizing the weighted evaluation matrix and the utility value matrix, and the difference weight row vector as the parameter to be solved. The first determining module is used to determine the maximum variance evaluation matrix using the interior point method based on the maximum variance evaluation optimization model and the evaluation index matrix. The second determining module is used to determine multiple evaluation values ​​corresponding to the multiple anti-interference methods based on the maximum difference evaluation matrix, the evaluation index matrix and the two-layer index, using the hierarchical entropy weight method. The multiple evaluation values ​​are used to indicate the anti-interference effect of the multiple anti-interference methods.

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