Method for compressing and decompressing electromagnetic scattering field data of a reference variable excitation source

Abstract

The application discloses a kind of electromagnetic scattering field data compression methods of parametric excitation source, comprising: for any scattering target, numerical simulation method is used to establish matrix equation;Solve the solution vector of the target under the action of a group of different parameters of excitation source, using greedy algorithm, the orthogonalization of solution vector group generated in step one, the vector group after orthogonalization, as the orthogonal basis of data compression;According to the numerical simulation method used in step one, calculate the scattering data corresponding to each orthogonal basis, and calculate the restoration matrix through these scattering data;Using orthogonal vector basis, the matrix left and right point multiplication established by numerical simulation method, form the reduced matrix;Inverse of reduced matrix obtains the data after compression.This application can adapt to target identification, detection, imaging multiple application scenarios, can greatly reduce the amount of redundant data, avoid the storage of large-scale matrix, flexible balance compression ratio and compression accuracy.

Description

Technical Field

[0001] This invention relates to electromagnetic scattering technology in the field of electrical engineering, and in particular to a method for compressing electromagnetic scattering field data of a parametric excitation source, and a method for decompressing electromagnetic scattering field data of a parametric excitation source corresponding to the electromagnetic scattering field data compression method of the parametric excitation source. Background Technology

[0002] In radar technology, target identification, detection, and imaging typically require a large amount of electromagnetic scattering field data to describe the electromagnetic properties of known targets. Traditional methods either directly store the scattering field data generated by the target or compress this data through interpolation. However, these methods are insufficient to meet the demands for higher precision and resolution scattering feature modeling. Furthermore, as radar frequencies increase, the scattering field data of a target grows quadratically with its electrical size. Therefore, efficiently and accurately storing and retrieving massive amounts of scattering data remains a major challenge in this field.

[0003] Related technical terms

[0004] A greedy algorithm is one that, when solving a problem, always makes the choice that seems best at the moment. In other words, it does not consider the overall optimal solution and obtains a locally optimal solution in some sense.

[0005] The Method of Moments (MoM) is a method for discretizing continuous equations into a system of algebraic equations, applicable to solving both differential and integral equations.

[0006] The Finite Element Method (FEM) is a numerical technique for finding approximate solutions to boundary value problems of partial differential equations. During the solution process, the entire problem domain is decomposed, and each subdomain becomes a simplified part; this simplified part is called a finite element.

[0007] Orthogonalization refers to transforming linearly independent... vector The process of transforming a system into an orthogonal system. Let {xn} be a finite number or countable number of linearly independent vectors in the inner product space H. Then there must exist an orthogonal system {en} in H such that for every positive integer n (when {xn} contains only m vectors, n≤m), xn is a linear combination of e1, e2, ..., en.

[0008] Normalization is a dimensionless processing method that transforms the absolute values ​​of physical system values ​​into relative values. It is an effective way to simplify calculations and reduce the size of quantities. Summary of the Invention

[0009] The summary of this invention introduces a series of simplified concepts, all of which are simplifications of existing technologies in the field, and will be further explained in detail in the detailed description section. This summary is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0010] The technical problem to be solved by the present invention is to provide a parametric excitation source electromagnetic scattering field data compression method that can adapt to various application scenarios such as target recognition, detection, and imaging, and can control the data compression ratio according to a given error threshold.

[0011] And a parametric excitation source electromagnetic scattering field data decompression method that can adapt to various application scenarios such as target recognition, detection, and imaging, and can control the data restoration accuracy according to a given error threshold.

[0012] To solve the above-mentioned technical problems, the present invention provides a method for compressing electromagnetic scattering field data of a parametric excitation source, comprising the following steps:

[0013] Step 1: For an arbitrary scattering target, establish the matrix equation using numerical simulation methods;

[0014] A·v=s(μ) (1)

[0015] Solve for the solution vector V = {v1, v2, ..., v} of the target under the action of a set of excitation sources s(μ) with different parameters μ. n};

[0016] Step 2: Using a greedy algorithm, orthogonalize the solution vector set V generated in Step 1. The orthogonalized vector set X = {x1, x2, ..., x...} n} will serve as the orthogonal basis for data compression;

[0017] Step 3: Calculate x for each orthogonal basis in Step 2 using the numerical simulation method employed in Step 1. n The corresponding scattering data r n And through these scattering data, R = {r1, r2, ..., r} was calculated. n This is called the restoration matrix R;

[0018] Step 4: Using the orthogonal vector basis X generated in Step 2, perform left and right dot products on the matrix established by the numerical simulation method in Step 1 to form a reduced-order matrix M.

[0019] M = X T ·A·X (2)

[0020] Among them, X TThe inverse of the reduced-order matrix M, which is the transpose of X, is called the inverse M of the reduced-order matrix. -1 M -1 That is, the compressed data.

[0021] Alternatively, the electromagnetic scattering field data compression method for the parametric excitation source, wherein the greedy algorithm has the following calculation process:

[0022] 1) Randomly select a vector from the sample set P, normalize it, and use it as the first basis vector x1;

[0023] 2) By weighted summation, each other sample vector in the sample set P is approximated using the vector basis x1;

[0024] 3) Calculate the approximation error ∈ P = ||s(μ) for each sample vector. i )-[s(μ i )·x1]x1||2,s(μ i ) is the activation vector, s(μ) η ) represents the sample vector;

[0025] 4) Select the maximum approximation error ∈ η The corresponding sample vector s(μ) η );

[0026] 5) Judgment∈ η If the value is less than the error threshold, the calculation ends.

[0027] 6) Set s(μ) η Orthogonalize, normalize to unit, and add to the basis vector group X;

[0028] 7) By weighted summation, the basis vector set X is used to approximate other vectors in the sample set P;

[0029] 8) Calculate the approximation error ∈ for each sample vector. P =||s(μ i )-s(μ i )·X T ·X||2;

[0030] 9) Select ∈ P The sample vector s(μ) corresponding to the maximum value η );

[0031] 10) Repeat steps 5) to 9).

[0032] Alternatively, in the electromagnetic scattering field data compression method for the parametric excitation source, step three involves processing r... n The calculation process is as follows;

[0033]

[0034] Where J is the current density, obtained from the orthogonal basis, j is the imaginary sign, ω is the angular frequency, and μ is the permeability. Let be the dyadic Green's function in free space.

[0035] Alternatively, in the electromagnetic scattering field data compression method for the parametric excitation source, step three involves processing r... n The calculation process is as follows

[0036]

[0037] Where J is the current density, which can be obtained from the orthogonal basis, j is the imaginary number, ω is the angular frequency, and μ is the permeability. Let be the dyadic Green's function in free space.

[0038] Alternatively, the electromagnetic scattering field data compression method of the parametric excitation source may be described, and the numerical simulation method may be the method of moments or the finite element method.

[0039] To address the aforementioned technical problems, this invention provides a method for decompressing electromagnetic scattering field data from a parametric excitation source. This method decompresses data obtained by the compression method described above for electromagnetic scattering field data from any of the parametric excitation sources, and includes the following steps:

[0040] Step 5: Decompress the file with specified parameter μ i When analyzing the scattering data corresponding to the excitation source, the excitation vector s(μ) is generated based on the excitation source. i );

[0041] Step 6: Perform matrix-vector multiplication with the orthogonal basis from Step 2 to obtain the dimension-reduced excitation vector e. i =X T ·s(μ i );

[0042] Step 7: Reduce the dimension of the excitation vector e i Perform matrix-vector multiplication with the inverse of the dimension-reduced matrix in step three.

[0043] b i =M -1 ·e i (3)

[0044] Obtain the reduced-dimensional solution vector b i The reduced-dimensional solution vector is multiplied by a matrix-vector multiplication with the restored matrix R from step three to obtain f. i =R·b i The decompression process is now complete.

[0045] Compared with the prior art, the present invention can achieve at least the following beneficial technical effects:

[0046] 1. The technical solution provided by this invention can significantly reduce the amount of redundant data and avoid storing large-scale matrices compared with direct storage or interpolation methods.

[0047] 2. The technical solution provided by this invention, which compares the difference method, can control the error threshold of the greedy algorithm when selecting orthogonal basis in step two, and flexibly balance the compression ratio and compression accuracy.

[0048] 3. The technical solution provided by this invention allows for arbitrary setting of the parameters of the excitation source to reconstruct electromagnetic scattering field data that meets the error threshold requirements.

[0049] 4. The technical solution provided by this invention has no requirements on the form of the excitation source, and has wide applicability. The excitation source can be a plane wave, spherical wave, cylindrical wave, Gaussian beam, point source, etc. There are also no requirements on the type of parameters; they can be coordinates, phase, azimuth, or intensity. It also has potential application value in the fields of antennas and electromagnetic compatibility. Attached Figure Description

[0050] The accompanying drawings are intended to illustrate the general characteristics of the methods, structures, and / or materials used in specific exemplary embodiments of the invention, supplementing the description in the specification. However, the drawings are schematic diagrams not drawn to scale and may not accurately reflect the precise structural or performance characteristics of any of the given embodiments. The drawings should not be construed as limiting or restricting the range of numerical values ​​or properties covered by exemplary embodiments of the invention. The invention will now be described in further detail with reference to the accompanying drawings and specific embodiments:

[0051] Figure 1 This is a schematic diagram of the process of this invention.

[0052] Figure 2 This is a diagram illustrating the error between the decompression result data and the actual data. Detailed Implementation

[0053] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can fully understand other advantages and technical effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through different specific embodiments, and the details in this specification can also be applied based on different viewpoints, with various modifications or changes made without departing from the overall design concept of the invention. It should be noted that, unless otherwise specified, the following embodiments and features can be combined with each other. The following exemplary embodiments of the present invention can be implemented in many different forms and should not be construed as being limited to the specific embodiments set forth herein. It should be understood that these embodiments are provided to make the disclosure of the present invention thorough and complete, and to fully convey the technical solutions of these exemplary embodiments to those skilled in the art.

[0054] First embodiment;

[0055] This invention provides a method for compressing electromagnetic scattering field data from a parametric excitation source, comprising the following steps:

[0056] Step 1: For an arbitrary scattering target, establish the matrix equation using numerical simulation methods;

[0057] A·v=s(μ) (1)

[0058] Solve for the solution vector V = {v1, v2, ..., v} of the target under the action of a set of excitation sources s(μ) with different parameters μ. n};

[0059] Step 2: Using a greedy algorithm, orthogonalize the solution vector set V generated in Step 1. The orthogonalized vector set X = {x1, x2, ..., x...} n} will serve as the orthogonal basis for data compression;

[0060] Step 3: Calculate x for each orthogonal basis in Step 2 using the numerical simulation method employed in Step 1. n The corresponding scattering data r n And through these scattering data, R = {r1, r2, ..., r} is calculated. n This is called the restoration matrix R;

[0061] Step 4: Using the orthogonal vector basis X generated in Step 2, perform left and right dot products on the matrix established by the numerical simulation method in Step 1 to form a reduced-order matrix M.

[0062] M = X T .A·X (2)

[0063] Among them, X T The inverse of the reduced-order matrix M, which is the transpose of X, is called the inverse M of the reduced-order matrix. -1M -1 That is, the compressed data.

[0064] Alternatively, the greedy algorithm described in the first embodiment above has the following calculation process:

[0065] 1) Randomly select a vector from the sample set P, normalize it, and use it as the first basis vector x1;

[0066] 2) By weighted summation, each other sample vector in the sample set P is approximated using the vector basis x1;

[0067] 3) Calculate the approximation error ∈ for each sample vector. P =||s(μ i )-[s(μ i )·x1]x1||2,s(μ i ) is the activation vector, s(μ) η ) represents the sample vector;

[0068] 4) Select the maximum approximation error ∈ η The corresponding sample vector s(μ) η );

[0069] 5) Judgment∈ η If the value is less than the error threshold, the calculation ends.

[0070] 6) Set s(μ) η Orthogonalize, normalize to unit, and add to the basis vector group X;

[0071] 7) By weighted summation, the basis vector set X is used to approximate other vectors in the sample set P;

[0072] 8) Calculate the approximation error ∈ for each sample vector. P =||s(μ i )-s(μ i )·X T ·X||2;

[0073] 9) Select ∈ P The sample vector s(μ) corresponding to the maximum value η );

[0074] 10) Repeat steps 5) to 9).

[0075] Alternatively, in the first embodiment described above, step three involves adjusting r. n The calculation process is as follows;

[0076]

[0077] Where J is the current density, obtained from the orthogonal basis, j is the imaginary sign, ω is the angular frequency, and μ is the permeability. Let be the dyadic Green's function in free space, and the numerical simulation method is the method of moments or the finite element method.

[0078] Second embodiment;

[0079] This invention provides a method for decompressing electromagnetic scattering field data from a parametric excitation source, which is used to decompress data obtained by compression in the first embodiment, and includes the following steps:

[0080] Step 5: Decompress the file with specified parameter μ i When analyzing the scattering data corresponding to the excitation source, the excitation vector s(μ) is generated based on the excitation source. i );

[0081] Step 6: Perform matrix-vector multiplication with the orthogonal basis from Step 2 of the first embodiment to obtain the dimension-reduced excitation vector e. i =X T ·s(μ i );

[0082] Step 7: Reduce the dimension of the excitation vector e i Perform matrix-vector multiplication on the inverse of the dimension-reduced matrix in step three of the first embodiment.

[0083] b i =M -1 ·e i (3)

[0084] Obtain the reduced-dimensional solution vector b i The reduced-dimensional solution vector is multiplied by a matrix-vector multiplication with the restored matrix R from step three of the first embodiment to obtain f. i =R·b i The decompression process is now complete.

[0085] This invention transforms the electromagnetic scattering field data of a parametric excitation source to obtain a data compression and decompression method. In this process, there are no requirements for the shape, size, quantity, or surrounding environment (space where the target is located) of the target, nor are there any requirements for the type or parameters of the source. Therefore, the method of this invention has strong universality when facing practical application scenarios.

[0086] Because the modeling method of this invention has strong universality, it is applicable to any target, any type of excitation source, and any type of excitation source parameter. This modeling method can be combined with software and has application value in the fields of target recognition, detection, and imaging, and also has application potential in the fields of antennas and electromagnetic compatibility.

[0087] Compared to conventional electromagnetic scattering data storage methods, the electromagnetic scattering field data compression and storage method of the present invention using a parametric excitation source can significantly reduce the amount of redundant data and avoid storing large-scale matrices. It allows control over the algorithm error threshold when selecting orthogonal bases, flexibly balancing the compression ratio and compression accuracy. The parameters of the excitation source can be arbitrarily set to reconstruct electromagnetic scattering field data that meets the error threshold requirements. There are no restrictions on the form of the excitation source; it can be a plane wave, spherical wave, cylindrical wave, Gaussian beam, point source, etc. There are also no restrictions on the type of parameters; they can be coordinates, phase, azimuth, or intensity. It also has potential application value in the fields of antennas and electromagnetic compatibility.

[0088] To verify the accuracy and efficiency of the electromagnetic scattering field data compression and storage method for the parametric excitation source of this invention, a missile model is selected as the verification example. The solution frequency is assumed to be 1.2 GHz, and its electrical dimensions are 1.8λ × 1.8λ × 11.2λ. The traditional method of moments (MoM) is used as the numerical method, with a plane wave selected as the excitation source. The azimuth angle θ and elevation angle φ of the plane wave propagation are selected as the parameters of the excitation source. It is assumed that the parameter sampling interval of the excitation source is 1 degree, at which point the total bistatic radar cross-section data is approximately 125 GB. Setting the error threshold of the greedy algorithm to 0.01, the data storage size after compression is only 114 MB, a reduction of three orders of magnitude, and the dimension of matrix M after dimensionality reduction is only 61. During decompression, the time required to restore the data is much less than 1 second, which meets the decompression time requirements of most application scenarios, and the error of the decompressed data is also controlled within the error threshold.

[0089] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It will also be understood that, unless expressly defined herein, terms such as those defined in a general dictionary shall be interpreted as having the meaning consistent with their meaning in the relevant field context, and not as having an idealized or overly formal meaning.

[0090] This invention has been described in detail, but these descriptions are not intended to limit the invention. Many modifications and alterations can be made by those skilled in the art without departing from the principles of this invention, and these should also be considered within the scope of protection of this invention.

Claims

1. A method for compressing electromagnetic scattering field data from a parametric excitation source, characterized in that, Includes the following steps: Step 1: For an arbitrary scattering target, establish the matrix equation using numerical simulation methods; （1） Solve for the target under a set of different parameters Motivation source Solution vector under action ; Step 2: Use a greedy algorithm to transform the solution vector set generated in Step 1. Orthogonalization is performed, resulting in a set of orthogonal vectors. This will serve as an orthogonal basis for data compression; Step 3: Calculate the values ​​of each orthogonal basis used in Step 2 using the numerical simulation method employed in Step 1. Corresponding scattering data And calculate using these scattering data This is called the restoration matrix. ; Step 4: Use the orthogonal vector basis generated in Step 2 The left and right dot products of the matrix established by the numerical simulation method in step one are used to form a reduced-order matrix. ; （2） Step 5: Unzip with specified parameters When the scattering data corresponding to the excitation source is obtained, an excitation vector is generated based on the excitation source. ; Step 6: Perform matrix-vector multiplication with the orthogonal basis from Step 2 to obtain the dimension-reduced excitation vector. ; Step 7: Reduce the dimension of the excitation vector Perform matrix-vector multiplication with the inverse of the dimension-reduced matrix in step three. (3) Obtain the dimensionality reduction solution vector The dimensionality-reduced solution vector is compared with the restoration matrix in step three. Perform matrix-vector multiplication to obtain Complete the decompression process; in, for The transpose of the reduced-order matrix Finding the inverse is called finding the inverse of a dimension-reduced matrix. , That is, the compressed data.

2. The method for compressing electromagnetic scattering field data from a parametric excitation source according to claim 1, characterized in that: The greedy algorithm has the following calculation process: 1) Randomly select a vector from the sample set P, normalize it, and use it as the first basis vector. ; 2) Using a vector basis through weighted accumulation. Approximate each other sample vector in the sample set P; 3) Calculate the approximation error for each sample vector. , For the activation vector, For sample vectors; 4) Select the maximum approximation error The corresponding sample vector ; 5) Judgment Is it less than the error threshold? The calculation ends when the condition is met. 6) Orthogonalize, normalize to unit, and add to the basis vector set. ; 7) Using a weighted summation with a set of basis vectors. Approximation Sample Set Other vectors in; 8) Calculate the approximation error for each sample vector. ; 9) Select The sample vector corresponding to the maximum value ; 10) Repeat steps 5) through 9).

3. The method for compressing electromagnetic scattering field data from a parametric excitation source according to claim 1, characterized in that: In step three, The calculation process is as follows; （4） in, For current density, Obtained from orthogonal basis, It is the symbol for imaginary numbers. Angular frequency, Permeability, Let be the dyadic Green's function in free space.

4. The method for compressing electromagnetic scattering field data from a parametric excitation source according to claim 1, characterized in that: The numerical simulation method is either the method of moments or the finite element method.

5. A computer-readable storage medium, characterized in that: It internally stores a program, which, when executed, implements the steps in the electromagnetic scattering field data compression method of the parametric excitation source according to any one of claims 1-4; And / or, it internally stores a program that, when executed, implements the steps in the method for decompressing electromagnetic scattering field data of the parametric excitation source according to any one of claims 1-4.

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