Graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing
Through precompletion processing and graph-guided Bayesian method, the problem of high-rank ocean sound field data matrix reconstruction is solved, and the sound field reconstruction effect with high precision and anti-noise is achieved.
Patent Information
- Application Number
- CN202411465575.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-21
- Publication Date
- 2025-09-02
- Estimated Expiration
- 2044-10-21
AI Technical Summary
The existing graph-guided Bayesian matrix completion method is mainly aimed at low-rank matrices and cannot be directly applied to the reconstruction of high-rank marine acoustic field data matrix, resulting in large reconstruction errors and cannot meet the reconstruction performance requirements.
The graph-guided Bayesian method with precompletion processing is adopted to reconstruct and reconstruct the ocean sound field data matrix through low-rank preprocessing and secondary low-rank precompletion processing, combined with Bayesian method and graph guidance technology.
It improves the accuracy and noise resistance of marine sound field reconstruction, and can achieve high-quality sound field reconstruction under sparse data and uneven distribution, enhancing the applicability and stability of the method.
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Figure CN119413271B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to an ocean sound field reconstruction method in the field of ocean sound fields, and in particular to a graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing. Background Art
[0002] The ocean plays a vital role in national defense and resource development. However, the propagation of electromagnetic waves in water is severely limited. Sound waves, due to their long propagation distance, have become the preferred carrier for ocean information transmission. Tasks such as underwater communications, navigation, target tracking, and environmental monitoring all rely on the propagation of sound waves. Accurately reconstructing the ocean acoustic field is crucial for the successful execution of these tasks. However, due to limitations in the coverage, resolution, and noise interference of measurement equipment, actual measured data is often sparse and unevenly distributed, failing to fully reflect the true state of the acoustic field.
[0003] Conventional sound field reconstruction methods include nearest neighbor interpolation, polynomial interpolation, and spline interpolation. While simple to use, these methods are not very effective at handling noise and leveraging global information. While methods such as iterative backprojection, convex set projection, and maximum a posteriori probability can improve reconstruction, they require high prior information, limiting their application scenarios.
[0004] Existing graph-guided Bayesian matrix completion methods primarily target low-rank matrices and are typically used to complete low-rank environmental field data. However, ocean acoustic data matrices typically have higher ranks, and directly applying this method to reconstruction results in large errors and fails to meet reconstruction performance requirements. Therefore, an improved method is needed to effectively handle the reconstruction of high-rank acoustic data matrices. Summary of the Invention
[0005] The purpose of the present invention is to provide a graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing to address the deficiencies of the existing technology.
[0006] The object of the present invention is achieved through the following technical solutions:
[0007] 1. A graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing
[0008] 1) Obtain ocean acoustic field profile observation matrix;
[0009] 2) Using the graph-guided Bayesian low-rank matrix completion method to perform low-rank pre-completion processing on the ocean acoustic field profile observation matrix, a pre-completion acoustic field matrix is obtained;
[0010] 3) Generate several reference matrix blocks based on the pre-complemented full sound field matrix, then traverse the pre-complemented full sound field matrix to find similar matrix blocks for each reference matrix block according to the size of each reference matrix block, and reconstruct the low-rank reconstruction matrix corresponding to all reference matrix blocks based on each reference matrix block and the corresponding similar matrix block;
[0011] 4) After performing secondary low-rank pre-completion processing on the low-rank reconstruction matrix corresponding to each reference matrix block, the reconstruction matrix corresponding to each reference matrix block is obtained, and the prediction results of each matrix block in all reconstruction matrices are returned to the corresponding position in the matrix to be completed of the ocean sound field profile, thereby obtaining the final sound field reconstruction matrix.
[0012] Said 2) is specifically:
[0013] Firstly, the sound field reconstruction target optimization function is constructed. Then, based on the ocean sound field profile observation matrix, the Bayesian method is used to solve the sound field reconstruction target optimization function to obtain the pre-completed sound field matrix.
[0014] In the above 2), the formula of the sound field reconstruction objective optimization function is as follows:
[0015]
[0016] Among them, λ, γ r and γ c are the first to third hyperparameters, X is the ocean acoustic profile matrix to be completed with dimension I*J, Y is the ocean acoustic profile observation matrix with dimension I*J, Ω is a mask matrix with dimension I*J composed of 0 and 1, and the element in the mask matrix Ω is 0, which means that the point corresponding to the element in the ocean acoustic profile observation matrix Y is not observed, and the element in the mask matrix Ω is 1, which means that the point corresponding to the element in the ocean acoustic profile observation matrix Y is observed; * represents the Hadamard product; L r is the row-wise graph Laplacian matrix, L c is the column-wise graph Laplacian matrix, T represents the transpose, || || * represents the nuclear norm, |||| F stands for the Frobenius norm and tr() stands for the trace of the matrix.
[0017] In the above 3), several reference matrix blocks are generated according to the pre-complemented full sound field matrix, specifically:
[0018] The pre-complemented full sound field matrix is divided into a plurality of reference matrix blocks of the same size, and the union of all the reference matrix blocks covers the pre-complemented full sound field matrix.
[0019] In the above 3), the low-rank reconstruction matrix corresponding to all reference matrix blocks is obtained according to each reference matrix block and the corresponding similar matrix block reconstruction, specifically:
[0020] After arranging the similarities of all similar matrix blocks of each reference matrix block, several similar matrix blocks and reference matrix blocks with the highest similarity are selected and rearranged and reconstructed to obtain the low-rank reconstruction matrix corresponding to each reference matrix block; after traversing, arranging and reconstructing all reference matrix blocks, the low-rank reconstruction matrix corresponding to all reference matrix blocks is obtained;
[0021] The method selects several similar matrix blocks and reference matrix blocks with the highest similarity, rearranges and reconstructs them, and obtains a low-rank reconstruction matrix corresponding to each reference matrix block, specifically:
[0022] For each reference matrix block, select the N similar matrix blocks with the highest similarity to it, rearrange all elements of the current reference matrix block into a row vector by row, and rearrange all elements of the N similar matrix blocks into corresponding N row vectors by row respectively, and then rearrange the row vectors corresponding to the current reference matrix block and the N similar matrix blocks into N+1 row vectors, so as to combine them into a new matrix and record it as the low-rank reconstruction matrix of the current reference matrix block.
[0023] In the above 4), after performing secondary low-rank pre-complement processing on the low-rank reconstruction matrix corresponding to each reference matrix block, the reconstruction matrix corresponding to each reference matrix block is obtained, specifically:
[0024] For the low-rank reconstruction matrix corresponding to each reference matrix block, all low-rank pre-complement elements are removed, and only the original elements in the ocean sound field profile observation matrix are retained to obtain a low-rank reconstruction matrix that only retains the original observation elements; then the low-rank reconstruction matrix that only retains the original observation elements is again low-rank pre-complemented using the graph-guided Bayesian low-rank matrix completion method to obtain the corresponding reconstruction matrix; the low-rank reconstruction matrices of each reference matrix block are traversed to obtain the reconstruction matrices corresponding to each reference matrix block, and then the restored results of each matrix block in each reconstruction matrix are returned to the corresponding positions in the ocean sound field profile matrix to be completed.
[0025] In the above 4), the calculation process of the final sound field reconstruction matrix includes:
[0026] The average of the repeated prediction results at each position in the ocean sound field profile matrix to be completed is calculated to obtain the final reconstruction prediction value of the position. After traversing and processing all the repeated prediction results in the ocean sound field profile matrix to be completed, the final sound field reconstruction matrix is obtained.
[0027] In the above 3), the calculation formula for the similarity between each reference matrix block and a corresponding similar matrix block is as follows:
[0028] Dist(P A ,P B )=||(P A-P B )*ω(P A )*ω(P B )|| F
[0029] Among them, Dist(P A ,P B ) represents the reference matrix block P A and similarity matrix block P B The similarity between them, ω(·) represents the weight of each element in the matrix block, * represents the Hadamard product, |||| F represents the Frobenius norm.
[0030] 2. A computer device
[0031] The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing when executing the computer program.
[0032] 3. A computer-readable storage medium
[0033] The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing.
[0034] 4. A computer program product
[0035] The computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing.
[0036] The beneficial effects of the present invention are:
[0037] 1. In view of the problem that the traditional graph-guided Bayesian matrix completion method is mainly applied to low-rank matrices and cannot be directly applied to the reconstruction of high-rank sound field data matrices, the present invention enhances the applicability of the method by introducing a low-rank preprocessing step, thereby significantly improving the reconstruction accuracy.
[0038] 2. Due to the high noise interference in the marine environment, sound field measurement data is often affected by noise. This invention uses Bayesian methods and low-rank preprocessing to effectively suppress noise interference, enhance the noise resistance of the reconstruction method, and ensure the stability and reliability of the reconstruction results.
[0039] 3. The present invention can maintain high reconstruction performance even when the observed data is sparse and unevenly distributed. The combination of low-rank preprocessing steps and graph-guided Bayesian completion enables the method to fully utilize the global and local information of the sound field data in environments with severe data loss, still achieving high-quality sound field reconstruction and improving the accuracy of the reconstruction results. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] Figure 1 This is a flow chart of the graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing proposed by the present invention.
[0041] Figure 2 It is a sound field map of the real complete data matrix within the selected area.
[0042] Figure 3 This is a sound field diagram that only shows partial observation data with 30% data sampling of the real complete sound field.
[0043] Figure 4 It is a predicted map after the sound field is reconstructed using the graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing.
[0044] Figure 5 It is a schematic diagram of searching for a similar matrix based on a reference matrix.
[0045] Figure 6 It is a schematic diagram of a method for reconstructing a reference matrix and a similarity matrix into a new matrix.
[0046] Figure 7 is the root mean square error between the reconstruction result of the method and the real sound field under four different sound source frequency conditions.
[0047] Figure 8 It is the structural similarity between the reconstruction results of the method and the real sound field under four different sound source frequency conditions. DETAILED DESCRIPTION
[0048] In order to make the purpose, technical solutions and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention.
[0049] This paper proposes a graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing. The method first uses the graph-guided Bayesian ocean sound field completion method to complete the observation data, then finds the similar matrices corresponding to each reference matrix and reconstructs them into new matrices. The graph-guided Bayesian ocean sound field reconstruction method is then used to complete and reconstruct the reconstructed matrices. After homing, the average value is calculated to obtain the final ocean sound field reconstruction.
[0050] like Figure 1As shown, the method proposed by the present invention includes the following steps:
[0051] 1) Obtain the ocean acoustic field profile observation matrix. Some data in the ocean acoustic field profile are missing;
[0052] 1) Specifically:
[0053] First, the area where the ocean sound field is to be reconstructed is selected, and relevant parameter information such as its sound velocity profile and seabed sediment is obtained. The location, depth and frequency information of the transmitting sound source are specified, and then the parabolic equation model is used to construct a complete and real ocean sound field in the area, and the following is obtained: Figure 2 The complete two-dimensional ocean sound propagation loss data matrix X of the specified area is shown in . Then, according to the specified sampling probability p, the sound field calculated by the parabolic equation model is randomly sampled. After sampling, only some data values of the sound field matrix are known, which is the original sound field that needs to be reconstructed and predicted, that is, the ocean sound field observation profile matrix, as shown in Figure 3 shown.
[0054] 2) Using the graph-guided Bayesian low-rank matrix completion method to perform low-rank pre-completion processing on the ocean acoustic field profile observation matrix, a complete two-dimensional pre-completion acoustic field matrix is obtained;
[0055] 2) Specifically:
[0056] First, the sound field reconstruction target optimization function is constructed. Then, based on the spatial correlation characteristics of the ocean sound field profile observation matrix and the observation data, the Bayesian method is used to solve the sound field reconstruction target optimization function, and a complete two-dimensional pre-completed sound field matrix is obtained. That is, the optimal ocean sound field profile matrix to be completed X is used as the pre-completed sound field matrix.
[0057] The formula of the sound field reconstruction objective optimization function is as follows:
[0058]
[0059] Among them, λ, γ r and γ c are the first to third hyperparameters, X is the ocean acoustic profile matrix to be completed with dimension I*J, Y is the ocean acoustic profile observation matrix with dimension I*J, Ω is a mask matrix with dimension I*J composed of 0 and 1, and the element in the mask matrix Ω is 0, which means that the point corresponding to the element in the ocean acoustic profile observation matrix Y is not observed, and the element in the mask matrix Ω is 1, which means that the point corresponding to the element in the ocean acoustic profile observation matrix Y is observed; * represents the Hadamard product; tr(X T L r X) is the regularization term of the row-by-row graph, tr(X T L c X) is the regularization term of the column-by-column graph; L ris the row-wise graph Laplacian matrix, L c is the column-wise graph Laplacian matrix, T represents the transpose, |||| * represents the nuclear norm, |||| F stands for the Frobenius norm and tr() stands for the trace of the matrix.
[0060] The row-by-row graph is constructed based on the spatial correlation assumption between the rows of the ocean acoustic profile matrix X to be completed and the prior knowledge of the observed data. Define an adjacency matrix A (dimension is I×I, I is the number of rows in the matrix), if the i-th row and the j-th row have spatial correlation, then A ij =1, otherwise A ij = 0. Through this adjacency matrix, construct the row-by-row graph Laplacian matrix L r , satisfying L r =DA, where D is the degree matrix, defined as D ii =Σ k A ik , where A ik Represents the spatial correlation between the i-th row and all possible related rows (i.e., the k-th row), which is used to traverse which rows the i-th row is related to. The regularization term of the row-by-row graph is expressed as tr(X T L r X). Similarly, we can get the column-by-column Laplace matrix L corresponding to the matrix X to be completed for the ocean sound field profile: c and the column-wise graph regularization term tr(X T L c X).
[0061] When solving the sound field reconstruction target optimization function, the Bayesian method is used to perform probability modeling, update, and inference on the parameters, and the alternating direction multiplier method (ADMM) is used to optimize the target function. Specifically:
[0062] The ocean acoustic field profile matrix X to be completed is represented by two factor matrices U and V:
[0063] X=UV T
[0064] Where U and V are factor matrices of dimensions I×K and J×K respectively, and K is a potential low-rank dimension. The prior distribution of the two factor matrices U and V is modeled as a multivariate Gaussian distribution, as follows:
[0065]
[0066] Λ=diag(θ)
[0067] Where p(U|θ) is the multivariate Gaussian distribution of the factor matrix U, p(V|θ) is the multivariate Gaussian distribution of the factor matrix V, the diagonal matrix Λ represents the column precision matrix, and the prior distribution of the precision parameter vector θ is set to the gamma distribution:
[0068]
[0069] Among them, p(θ) represents the prior distribution of the precision parameter vector, θ k represents the precision parameter of the i-th column of the factor matrix (U or V), c0 and d0 are the fourth and fifth hyperparameters respectively.
[0070] Then, the optimization problem is solved using the alternating direction method of multipliers (ADMM).
[0071] 3) Generate several reference matrix blocks based on the pre-completed sound field matrix, and then traverse the pre-completed sound field matrix to find similar matrix blocks for each reference matrix block according to the size of each reference matrix block. Different similar matrix blocks may overlap. Reconstruct each reference matrix block and the corresponding similar matrix block to obtain the low-rank reconstruction matrix corresponding to all reference matrix blocks;
[0072] 3) Specifically:
[0073] Step 1: Divide the pre-filled full sound field matrix into multiple reference matrix blocks of the same size. Different reference matrix blocks may overlap, and the union of all reference matrix blocks covers all element positions of the pre-filled full sound field matrix. Figure 5 As shown, the specific reference matrix selection scheme is: Matrix It is the pre-complementary full sound field matrix of the I×J dimensional measurement matrix Y, and the reference matrix block P i,j It is a full matrix of pre-filled sound field The size of the matrix block is s×s, and the element position of the upper left corner of the reference matrix block is defined as (i, j). Then each element (i, j) in the following set corresponds to a reference matrix block:
[0074] (i,j)∈{1,...,I-s+1}×{1,...,J-s+1}
[0075] Then, the subsequent second and third steps are performed one by one on all selected reference matrix blocks.
[0076] Step 2: Pre-fill the full matrix of the sound field Search all the s×s matrix blocks in the current reference matrix block one by one The top N similarity matrix blocks with the highest similarity, when performing the similarity measurement distance l2, give the original elements in the original observation matrix a weight of ω A, the weight assigned to the elements that are not in the original measurement matrix but are reconstructed in the full matrix of the pre-filled sound field is ω B , the reference matrix block and similar matrix block for comparison are P A and P B , then the calculation formula of weighted distance l2 is as follows:
[0077] Dist(P A ,P B )=||(P A -P B )*ω(P A )*ω(P B )|| F
[0078] Among them, Dist(P A ,P B ) represents the reference matrix block P A and similarity matrix block P B The similarity between them, ω(·) represents the weight of each element in the matrix block, * represents the Hadamard product, ‖‖ F stands for Frobenius norm.
[0079] Step 3: After arranging the similarities of all similar matrix blocks of each reference matrix block, select several similar matrix blocks and the reference matrix block with the highest similarity and rearrange and reconstruct them to obtain the low-rank reconstruction matrix corresponding to each reference matrix block; after traversing, arranging and reconstructing all reference matrix blocks, obtain the low-rank reconstruction matrix corresponding to all reference matrix blocks;
[0080] Among them, several similar matrix blocks and reference matrix blocks with the highest similarity are selected for rearrangement and reconstruction to obtain the low-rank reconstruction matrix corresponding to each reference matrix block, specifically:
[0081] For each reference matrix block, select the N similar matrix blocks with the highest similarity to it, rearrange all elements of the current reference matrix block into a row vector by row, and rearrange all elements of the N similar matrix blocks into corresponding N row vectors by row, and then rearrange the row vectors corresponding to the current reference matrix block and the N similar matrix blocks into N+1 row vectors, so as to form a new matrix and record it as the low-rank reconstruction matrix of the current reference matrix block, as follows: Figure 6 shown.
[0082] After traversing and processing each reference matrix block, a low-rank reconstruction matrix corresponding to each reference matrix block is obtained.
[0083] 4) After performing secondary low-rank pre-completion processing on the low-rank reconstruction matrix corresponding to each reference matrix block, the reconstruction matrix corresponding to each reference matrix block is obtained, and the prediction results of each matrix block in all reconstruction matrices are returned to the corresponding position in the matrix to be completed of the ocean sound field profile, thereby obtaining the final sound field reconstruction matrix, such as Figure 4 As shown. The other parameters of the fixed scene remain unchanged, and the sound source frequencies are set to 50Hz, 100Hz, 200Hz, and 400Hz respectively. The method obtains the following under different sound source frequency conditions: Figure 4 The final sound field reconstruction matrix shown is the same as Figure 2 The root mean square error RMSE and structural similarity SSIM between the real sound field matrices shown are as follows: Figure 7 and Figure 8 shown.
[0084] Among them, after performing secondary low-rank pre-complement processing on the low-rank reconstruction matrix corresponding to each reference matrix block, the reconstruction matrix corresponding to each reference matrix block is obtained, specifically:
[0085] For the low-rank reconstruction matrix corresponding to each reference matrix block, all low-rank pre-complement elements therein, i.e., the elements calculated in 2), are removed, and only the original elements in the ocean sound field profile observation matrix are retained to obtain a low-rank reconstruction matrix that only retains the original observation elements; then, the low-rank reconstruction matrix that only retains the original observation elements is subjected to low-rank pre-complement processing again using the graph-guided Bayesian low-rank matrix completion method to obtain the corresponding reconstruction matrix, i.e., the ocean sound field profile observation matrix Y of the sound field reconstruction objective optimization function in 2) is replaced with the low-rank reconstruction matrix that only retains the original observation elements, and the row-by-row graph Laplacian matrix L is updated. r , column-wise graph Laplacian matrix L c , the optimal ocean sound field profile to be completed matrix X obtained by solving the problem is used as the reconstruction matrix corresponding to the low-rank reconstruction matrix that only retains the original observation elements; the low-rank reconstruction matrix of each reference matrix block is traversed to obtain the reconstruction matrix corresponding to each reference matrix block, and then the restored results of each matrix block in each reconstruction matrix are returned to the corresponding position in the ocean sound field profile to be completed matrix.
[0086] The calculation process of the final sound field reconstruction matrix includes:
[0087] The average of the repeated prediction results at each position in the ocean sound field profile matrix to be completed is calculated to obtain the final reconstruction prediction value of the position. After traversing and processing all the repeated prediction results in the ocean sound field profile matrix to be completed, the final sound field reconstruction matrix is obtained.
[0088] Finally, it should be noted that the above embodiments and explanations are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. It should be understood by those skilled in the art that modifications or equivalent substitutions to the technical solutions of the present invention may be made without departing from the spirit and scope of the technical solutions disclosed herein, and all such modifications or equivalent substitutions shall be encompassed within the scope of protection of the claims of the present invention.
Claims
1. A graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing, characterized in that: The steps include: 1) Obtain ocean acoustic field profile observation matrix; 2) Using the graph-guided Bayesian low-rank matrix completion method to perform low-rank pre-completion processing on the ocean acoustic field profile observation matrix, a pre-completion acoustic field matrix is obtained; 3) Generate several reference matrix blocks based on the pre-complemented full sound field matrix, then traverse the pre-complemented full sound field matrix to find similar matrix blocks for each reference matrix block according to the size of each reference matrix block, and reconstruct the low-rank reconstruction matrix corresponding to all reference matrix blocks based on each reference matrix block and the corresponding similar matrix block; 4) After performing secondary low-rank pre-completion processing on the low-rank reconstruction matrix corresponding to each reference matrix block, the reconstruction matrix corresponding to each reference matrix block is obtained, and the prediction results of each matrix block in all reconstruction matrices are returned to the corresponding position in the matrix to be completed of the ocean sound field profile, thereby obtaining the final sound field reconstruction matrix.
2. The graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to claim 1 is characterized in that: Said 2) is specifically: Firstly, the sound field reconstruction target optimization function is constructed. Then, based on the ocean sound field profile observation matrix, the Bayesian method is used to solve the sound field reconstruction target optimization function to obtain the pre-completed sound field matrix.
3. The graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to claim 1 is characterized in that: In the above 2), the formula of the sound field reconstruction objective optimization function is as follows: Among them, λ, γ r and γ c are the first to third hyperparameters, X is the ocean acoustic profile matrix to be completed with dimension I*J, Y is the ocean acoustic profile observation matrix with dimension I*J, Ω is a mask matrix with dimension I*J composed of 0 and 1, and the element in the mask matrix Ω is 0, which means that the point corresponding to the element in the ocean acoustic profile observation matrix Y is not observed, and the element in the mask matrix Ω is 1, which means that the point corresponding to the element in the ocean acoustic profile observation matrix Y is observed; * represents the Hadamard product; L r is the row-wise graph Laplacian matrix, L c is the column-wise graph Laplacian matrix, T represents the transpose, || || * represents the nuclear norm, || || F stands for the Frobenius norm and tr() stands for the trace of the matrix.
4. The graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to claim 1 is characterized in that: In the above 3), several reference matrix blocks are generated according to the pre-complemented full sound field matrix, specifically: The pre-complemented full sound field matrix is divided into a plurality of reference matrix blocks of the same size, and the union of all the reference matrix blocks covers the pre-complemented full sound field matrix.
5. The graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to claim 1 is characterized in that: In the above 3), the low-rank reconstruction matrix corresponding to all reference matrix blocks is obtained according to each reference matrix block and the corresponding similar matrix block reconstruction, specifically: After arranging the similarities of all similar matrix blocks of each reference matrix block, several similar matrix blocks and reference matrix blocks with the highest similarity are selected and rearranged and reconstructed to obtain the low-rank reconstruction matrix corresponding to each reference matrix block; after traversing, arranging and reconstructing all reference matrix blocks, the low-rank reconstruction matrix corresponding to all reference matrix blocks is obtained.
6. The graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to claim 5 is characterized in that: The method selects several similar matrix blocks and reference matrix blocks with the highest similarity, rearranges and reconstructs them, and obtains a low-rank reconstruction matrix corresponding to each reference matrix block, specifically: For each reference matrix block, select the N similar matrix blocks with the highest similarity to it, rearrange all elements of the current reference matrix block into a row vector by row, and rearrange all elements of the N similar matrix blocks into corresponding N row vectors by row respectively, and then rearrange the row vectors corresponding to the current reference matrix block and the N similar matrix blocks into N+1 row vectors, so as to combine them into a new matrix and record it as the low-rank reconstruction matrix of the current reference matrix block.
7. The graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to claim 1 is characterized in that: In the above 4), after performing secondary low-rank pre-complement processing on the low-rank reconstruction matrix corresponding to each reference matrix block, the reconstruction matrix corresponding to each reference matrix block is obtained, specifically: For the low-rank reconstruction matrix corresponding to each reference matrix block, all low-rank pre-complement elements are removed, and only the original elements in the ocean sound field profile observation matrix are retained to obtain a low-rank reconstruction matrix that only retains the original observation elements; then the low-rank reconstruction matrix that only retains the original observation elements is again low-rank pre-complemented using the graph-guided Bayesian low-rank matrix completion method to obtain the corresponding reconstruction matrix; the low-rank reconstruction matrices of each reference matrix block are traversed to obtain the reconstruction matrices corresponding to each reference matrix block, and then the restored results of each matrix block in each reconstruction matrix are returned to the corresponding positions in the ocean sound field profile matrix to be completed.
8. The graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to claim 1 is characterized in that: In the above 4), the calculation process of the final sound field reconstruction matrix includes: The average of the repeated prediction results at each position in the ocean sound field profile matrix to be completed is calculated to obtain the final reconstruction prediction value of the position. After traversing and processing all the repeated prediction results in the ocean sound field profile matrix to be completed, the final sound field reconstruction matrix is obtained.
9. The graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to claim 5, characterized in that: In the above 3), the calculation formula for the similarity between each reference matrix block and a corresponding similar matrix block is as follows: Dist(P A ,P B )=||(P A -P B )*ω(P A )*ω(P B )|| F Among them, Dist(P A ,P B ) represents the reference matrix block P A and similarity matrix block P B The similarity between them, ω(·) represents the weight of each element in the matrix block, * represents the Hadamard product, || || F represents the Frobenius norm.
10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the graph-guided Bayesian ocean sound field reconstruction method based on pre-completion processing according to any one of claims 1 to 9 is implemented.