Method, device, equipment and medium for generating moving target RCS simulation data

Abstract

The present invention relates to the field of radar detection technology, and in particular to a method, device, equipment and medium for generating RCS simulation data of a moving target, wherein the method comprises: constructing a first covariance matrix of target RCS data samples, wherein the RCS data samples include RCS values ​​of the target at different viewing angles and the confidence of the RCS values; generating a second covariance matrix based on the target viewing angle and the first covariance matrix; the target viewing angle is related to the motion trajectory to be simulated of the target; performing Cholesky decomposition on the second covariance matrix to obtain a lower triangular matrix corresponding to the second covariance matrix; and generating RCS simulation data of the target based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle. This solution can improve the accuracy of the generated target RCS simulation data.

Description

Technical Field

[0001] Embodiments of the present invention Embodiments of the present invention relate to the field of radar detection technology, and more particularly to a method, device, equipment and medium for generating RCS simulation data of a moving target Background Art

[0002] Radar Cross Section (RCS) is a physical quantity that measures the intensity of electromagnetic wave scattering by a target and is an important input parameter of a radar target simulator. A radar target simulator is used to simulate target echo signals, and the authenticity of its simulation results is closely related to the accuracy of the input RCS simulation data.

[0003] In the related art, the RCS simulation data of moving targets used by radar target simulators are fixed values. However, due to the limitations of the target RCS modeling calculation algorithm and the accuracy of input information, the RCS data of many moving targets cannot be accurately obtained. Therefore, the use of fixed RCS simulation data cannot reflect the changing boundaries of the moving target RCS, which in turn leads to poor authenticity of the echo signal simulated by the radar target simulator.

[0004] Therefore, there is an urgent need for a method, device, equipment and medium for generating moving target RCS simulation data to solve the above technical problems. Summary of the invention

[0005] Based on the problem that the target RCS simulation data is not accurate enough, the embodiments of the present invention provide a method, device, equipment and medium for generating target RCS simulation data, which can improve the accuracy of the generated target RCS simulation data.

[0006] In a first aspect, an embodiment of the present invention provides a method for generating moving target RCS simulation data, comprising:

[0007] Constructing a first covariance matrix of target RCS data samples, wherein the RCS data samples include RCS values ​​of the target at different viewing angles and confidence levels of the RCS values;

[0008] Based on the target viewing angle and the first covariance matrix, a second covariance matrix is ​​generated; the target viewing angle is related to the motion trajectory of the target to be simulated;

[0009] Performing Cholesky decomposition on the second covariance matrix to obtain a lower triangular matrix corresponding to the second covariance matrix;

[0010] The target RCS simulation data is generated based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle.

[0011] In a possible design, each viewing angle corresponds to a plurality of RCS values;

[0012] The constructing a first covariance matrix of the target RCS data sample includes:

[0013] Determine the average of a plurality of RCS values ​​corresponding to each viewing angle;

[0014] Constructing a third covariance matrix of the target RCS data sample based on a plurality of RCS values ​​corresponding to each of the viewing angles and a mean of the plurality of RCS values;

[0015] Based on the confidence of the RCS value corresponding to each viewing angle, the third covariance matrix is ​​corrected to obtain the first covariance matrix of the target RCS data sample.

[0016] In one possible design, the third covariance matrix s(θ 1 ,φ 1 θ 2 ,φ 2 ) is calculated as:

[0017]

[0018] Where θ,υ represent the pitch angle and azimuth angle of the viewing direction in the target coordinate system; E is the mathematical expectation, the superscript * is the complex conjugate; σ 0 (θ 1 ,φ 1 ) is a random variable, indicating that the pitch angle is θ 1 , azimuth is φ 1 The corresponding target RCS value; The pitch angle is θ 1 , azimuth is φ 1 The average value of the corresponding target RCS value; σ 0 * (θ 2 ,φ 2 ) is a random variable, indicating that the pitch angle is θ 2 , azimuth is φ 2 The complex conjugate of the corresponding target RCS value; The pitch angle is θ 2 , azimuth is φ 2 The complex conjugate of the mean value of the corresponding target RCS value;

[0019] The first covariance matrix Σ(θ 1 ,φ 1 θ 2 ,φ 2 ) is calculated as:

[0020] Σ(θ 1 ,φ 1 θ 2 ,φ 2 )=s(θ 1 ,φ 1 θ 2 ,φ 2 ) / p c1 / p c2

[0021] In the formula, p c1 、p c2 The viewing angles are (θ 1 ,φ 1 ) and (θ 2 ,φ 2 ) The confidence level of the RCS value corresponding to .

[0022] In a possible design, generating a second covariance matrix based on the target viewing angle and the first covariance matrix includes:

[0023] An element corresponding to the target viewing angle is selected from the first covariance matrix to generate a second covariance matrix.

[0024] In one possible design, the second covariance matrix is ​​equal to the product of the lower triangular matrix and the transpose of the lower triangular matrix.

[0025] In a possible design, generating RCS simulation data of the target based on the target viewing angle, the lower triangular matrix, and the RCS value corresponding to the target viewing angle includes:

[0026] Generate a first vector n, wherein the first vector is composed of the same number of independent and identically distributed Gaussian distributions as the number of the target viewing angles;

[0027] Generate a second vector μ, wherein the second vector is composed of the mean of the RCS values ​​corresponding to the target viewing angle;

[0028] Based on the first vector n, the second vector μ and the transpose L of the lower triangular matrix T , generate the RCS simulation data σ of the target 0 .

[0029] In one possible design, the target RCS simulation data σ 0 It is generated by the following formula:

[0030] σ 0 =μ+L T n.

[0031] In a second aspect, an embodiment of the present invention further provides a device for generating moving target RCS simulation data, including:

[0032] A construction module, used to construct a first covariance matrix of target RCS data samples, wherein the RCS data samples include RCS values ​​of the target at different viewing angles and confidence levels of the RCS values;

[0033] A first generating module, configured to generate a second covariance matrix based on a target viewing angle and the first covariance matrix; the target viewing angle is related to a motion trajectory of the target to be simulated;

[0034] A decomposition module, used for performing Cholesky decomposition on the second covariance matrix to obtain a lower triangular matrix corresponding to the second covariance matrix;

[0035] The second generating module is used to generate the target RCS simulation data based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle.

[0036] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0037] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method described in any embodiment of this specification.

[0038] The embodiment of the present invention provides a method, device, electronic device and storage medium for generating RCS simulation data of a moving target. First, a first covariance matrix of a target RCS data sample is constructed. The construction process of the matrix integrates the correlation of RCS values ​​at different viewing angles (i.e., angular domain correlation) and the relationship with confidence. Then, according to the target viewing angle and the first covariance matrix, a second covariance matrix is ​​generated. The second covariance matrix corresponds to the target motion trajectory to be simulated. Then, the second covariance matrix is ​​subjected to Cholesky decomposition to obtain a lower triangular matrix corresponding to the second covariance matrix. Finally, based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle, the RCS simulation data of the target is generated. Since the generation process of the RCS simulation data integrates the confidence and angular domain correlation of the RCS value, the generated target RCS simulation data has high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 is a flow chart of a method for generating RCS simulation data of a moving target provided by an embodiment of the present invention;

[0041] Figure 2 is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0042] Figure 3 It is a structural diagram of a device for generating moving target RCS simulation data provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0044] As mentioned above, using a certain value for the target RCS simulation data will affect the authenticity of the echo signal simulated by the radar target simulator.

[0045] To address this problem, the inventors propose that the confidence and angular domain correlation of the RCS value can be integrated to generate target RCS simulation data, so as to improve the accuracy of the target RCS simulation data.

[0046] The specific implementation of the above concept is described below.

[0047] Please refer to Figure 1 , an embodiment of the present invention provides a method for generating moving target RCS simulation data, the method comprising:

[0048] Step 100, constructing a first covariance matrix of target RCS data samples, where the RCS data samples include RCS values ​​of the target at different viewing angles and confidence levels of the RCS values;

[0049] Step 102, generating a second covariance matrix based on the target viewing angle and the first covariance matrix; the target viewing angle is related to the target motion trajectory to be simulated;

[0050] Step 104, performing Cholesky decomposition on the second covariance matrix to obtain a lower triangular matrix corresponding to the second covariance matrix;

[0051] Step 106, generating target RCS simulation data based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle.

[0052] In an embodiment of the present invention, a first covariance matrix of a target RCS data sample is first constructed, and the construction process of the matrix integrates the correlation of RCS values ​​at different viewing angles (i.e., angular domain correlation) and the relationship with the confidence. Then, according to the target viewing angle and the first covariance matrix, a second covariance matrix is ​​generated, and the second covariance matrix corresponds to the target motion trajectory to be simulated. Then, the second covariance matrix is ​​subjected to Cholesky decomposition to obtain a lower triangular matrix corresponding to the second covariance matrix. Finally, based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle, the target RCS simulation data is generated. Since the generation process of the RCS simulation data integrates the confidence and angular domain correlation of the RCS value, the generated target RCS simulation data has high accuracy.

[0053] Described below Figure 1 How the various steps are performed.

[0054] First, for step 100, a first covariance matrix of target RCS data samples is constructed, where the RCS data samples include RCS values ​​of the target at different viewing angles and confidence levels of the RCS values.

[0055] In this step, the RCS data samples are target RCS data samples within the radar angle measurement error and target RCS error range, which can be either continuous samples or discrete samples. The RCS data samples may include many viewing angles, each viewing angle includes corresponding azimuth and elevation angles, and the construction process of the first covariance matrix incorporates the correlation of RCS values ​​under different viewing angles (i.e., angular domain correlation) and the relationship with confidence. Among them, regarding the determination of confidence, the domestic radar target characteristic laboratory has formed a procedure file approved by the laboratory, completed the implementation of partial electromagnetic model and measurement system verification, verification and confirmation (VV&A), improved the confidence of the target characteristic model and data, and can quantitatively give the confidence of the target RCS value. Therefore, the confidence used in this step has a high accuracy and can be used for the calculation of the first covariance matrix.

[0056] In some embodiments, each viewing angle corresponds to several RCS values;

[0057] Construct the first covariance matrix of the target RCS data sample, including:

[0058] Determine the mean of several RCS values ​​corresponding to each viewing angle;

[0059] Based on a number of RCS values ​​corresponding to each viewing angle and the average of a number of RCS values, a third covariance matrix of the target RCS data sample is constructed;

[0060] Based on the confidence of the RCS value corresponding to each viewing angle, the third covariance matrix is ​​corrected to obtain the first covariance matrix of the target RCS data sample.

[0061] In this embodiment, for discrete samples, at least five RCS values ​​are taken for each viewing angle to ensure the accuracy of the calculation. Of course, the more RCS values ​​corresponding to each viewing angle, the higher the calculation accuracy, but the amount of calculation will increase accordingly. Users can determine the number of RCS values ​​according to their own needs, and this application does not make any specific limitations.

[0062] In some embodiments, the third covariance matrix s(θ 1 ,υ 1 θ 2 ,φ 2 ) is calculated as:

[0063]

[0064] Where θ,υ represent the pitch angle and azimuth angle of the viewing direction in the target coordinate system; E is the mathematical expectation, the superscript * is the complex conjugate; σ 0 (θ 1 ,φ 1 ) is a random variable, indicating that the pitch angle is θ 1 , azimuth is φ 1 The corresponding target RCS value; The pitch angle is θ 1 , azimuth is φ 1 The average value of the corresponding target RCS value; σ 0 * (θ 2 ,φ 2 ) is a random variable, indicating that the pitch angle is θ 2 , azimuth is φ 2 The complex conjugate of the corresponding target RCS value; The pitch angle is θ 2 , azimuth is φ 2 The complex conjugate of the mean value of the corresponding target RCS value;

[0065] The first covariance matrix ∑(θ 1 ,φ 1 θ 2 ,φ 2 ) is calculated as:

[0066] Σ(θ 1 ,φ 1 θ 2 ,φ 2 )=s(θ 1 ,φ 1 θ 2 ,φ 2 ) / p c1 / p c2

[0067] In the formula, p c1 、p c2 The viewing angles are (θ 1 ,φ 1 ) and (θ 2 ,φ 2 ) The confidence level of the RCS value corresponding to .

[0068] Then, for step 102, a second covariance matrix is ​​generated based on the target viewing angle and the first covariance matrix; the target viewing angle is related to the target motion trajectory to be simulated.

[0069] In this step, the target viewing angle can be determined according to the target's simulated motion trajectory. The target viewing angle can be determined at equal intervals, or it can be determined based on the importance of the area, with dense values ​​in more important areas and sparse values ​​in other areas. This application does not make any specific limitations on the value-taking method.

[0070] In some embodiments, generating a second covariance matrix based on the target viewing angle and the first covariance matrix includes:

[0071] The elements corresponding to the target viewing angles are selected from the first covariance matrix to generate the second covariance matrix. For example, if the first covariance matrix contains 100 elements and there are 50 target viewing angles, the elements corresponding to the 50 viewing angles are selected from the first covariance matrix to generate the second covariance matrix. It can be seen that the second covariance matrix is ​​a matrix corresponding to the motion trajectory of the target. The second covariance matrix is ​​recorded as A(θ 1 ,φ 1 θ 2 ,φ 2 ) / trajectory.

[0072] Next, with respect to step 104 , Cholesky decomposition is performed on the second covariance matrix to obtain a lower triangular matrix corresponding to the second covariance matrix.

[0073] In some implementations, the second covariance matrix is ​​equal to the product of the lower triangular matrix L and the transposed LT of the lower triangular matrix, and the specific expression is:

[0074] A(θ 1,φ 1 θ 2 ,φ 2 ) / trajectory=LL T

[0075] Specifically, the second covariance matrix A(θ 1 ,φ 1 θ 2 ,φ 2 ) / The Cholesky decomposition algorithm of the trajectory is shown as follows:

[0076]

[0077]

[0078] In the formula, l jj is the j-th row and j-th column element of the lower triangular matrix L; l ij is the i-th row and j-th column element of the lower triangular matrix L; l jk is the j-th row and k-th column element of the lower triangular matrix L; l ik is the i-th row and k-th column element of the lower triangular matrix L; a jj is the second covariance matrix A(θ 1 ,φ 1 θ 2 ,φ 2 ) / track element in row j and column j; a ij is the second covariance matrix A(θ 1 ,φ 1 θ 2 ,φ 2 ) / Trace the element in row i and column j.

[0079] Finally, for step 106, based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle, the RCS simulation data of the target is generated.

[0080] In some implementations, step 106 includes:

[0081] Step A1, generating a first vector n, the first vector n is composed of the same number of independent and identically distributed Gaussian distributions as the number of target viewing angles; in some embodiments, n ~ iidN (0, 1).

[0082] Step A2, generating a second vector μ, the second vector being composed of the mean of the RCS values ​​corresponding to the target viewing angle;

[0083] Step A3, based on the first vector n, the second vector μ and the transpose L of the lower triangular matrix T , generate the target RCS simulation data σ 0 .

[0084] In step A1, the first vector n=(n 1 ,n 2 ,…,n i ,…), each element in n is an independent and identically distributed pseudo-random number that follows the Gaussian distribution N(0,1), which can be obtained through the random number generation library of programming languages ​​such as C++ and Python.

[0085] In some embodiments, the target RCS simulation data σ 0 It is generated by the following formula:

[0086] σ 0 =μ+L T n.

[0087] In this embodiment, σ 0 The mean is μ, and the second covariance matrix is ​​A(θ 1 ,φ 1 θ 2 ,φ 2 ) / trajectory of a multivariate Gaussian random variable.

[0088] like Figure 2 , Figure 3 As shown, an embodiment of the present invention provides a device for generating RCS simulation data of a moving target. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown, it is a hardware architecture diagram of an electronic device where a device for generating RCS simulation data of a moving target provided by an embodiment of the present invention is located, except Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, the CPU of the electronic device in which it is located reads the corresponding computer program in the non-volatile memory into the internal memory and runs it. This embodiment provides a device for generating RCS simulation data of a moving target, including:

[0089] A construction module 300 is used to construct a first covariance matrix of target RCS data samples, where the RCS data samples include RCS values ​​of the target at different viewing angles and confidence levels of the RCS values;

[0090] A first generating module 302 is used to generate a second covariance matrix based on a target viewing angle and a first covariance matrix; the target viewing angle is related to a motion trajectory of the target to be simulated;

[0091] A decomposition module 304 is used to perform Cholesky decomposition on the second covariance matrix to obtain a lower triangular matrix corresponding to the second covariance matrix;

[0092] The second generating module 306 is used to generate target RCS simulation data based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle.

[0093] In an embodiment of the present invention, the construction module 300 can be used to execute step 100 in the above method embodiment, the first generation module 302 can be used to execute step 102 in the above method embodiment, the decomposition module 304 can be used to execute step 104 in the above method embodiment, and the second generation module 306 can be used to execute step 106 in the above method embodiment.

[0094] In some embodiments, each viewing angle corresponds to several RCS values;

[0095] Building block 300 is used to perform:

[0096] Determine the mean of several RCS values ​​corresponding to each viewing angle;

[0097] Based on a number of RCS values ​​corresponding to each viewing angle and the average of a number of RCS values, a third covariance matrix of the target RCS data sample is constructed;

[0098] Based on the confidence of the RCS value corresponding to each viewing angle, the third covariance matrix is ​​corrected to obtain the first covariance matrix of the target RCS data sample.

[0099] In some embodiments, the third covariance matrix s(θ 1 ,φ 1 θ 2 ,φ 2 ) is calculated as:

[0100]

[0101] Where θ and φ represent the elevation and azimuth angles of the target coordinate system; E is the mathematical expectation, and the superscript * is the complex conjugate; σ 0 (θ 1 ,φ 1 ) is a random variable, indicating that the pitch angle is θ 1 , azimuth is φ 1 The corresponding target RCS value; The pitch angle is θ 1 , azimuth is φ 1 The average value of the corresponding target RCS value; σ 0 * (θ 2 ,φ2 ) is a random variable, indicating that the pitch angle is θ 2 , azimuth is φ 2 The complex conjugate of the corresponding target RCS value; The pitch angle is θ 2 , azimuth is φ 2 The complex conjugate of the mean value of the corresponding target RCS value;

[0102] The first covariance matrix ∑(θ 1 ,φ 1 θ 2 ,φ 2 ) is calculated as:

[0103] ∑(θ 1 ,φ 1 θ 2 ,φ 2 )=s(θ 1 ,φ 1 θ 2 ,φ 2 ) / p c1 / p c2

[0104] In the formula, p c1 、p c2 The viewing angles are (θ 1 ,φ 1 ) and (θ 2 ,φ 2 ) The confidence level of the RCS value corresponding to .

[0105] In some implementations, the first generation module 302 is configured to perform:

[0106] The elements corresponding to the target viewing angles are selected from the first covariance matrix to generate a second covariance matrix.

[0107] In some implementations, the second covariance matrix is ​​equal to the product of the lower triangular matrix and the transpose of the lower triangular matrix.

[0108] In some implementations, the second generation module 306 is configured to execute:

[0109] Generate a first vector n, the first vector being composed of independent and identically distributed Gaussian distributions of the same number as the number of target viewing angles;

[0110] Generate a second vector μ, which is composed of the mean of the RCS values ​​corresponding to the target viewing angle;

[0111] Based on the first vector n, the second vector μ and the transpose L of the lower triangular matrix T , generate the target RCS simulation data σ 0 .

[0112] In some embodiments, the target RCS simulation data σ 0 It is generated by the following formula:

[0113] σ 0 =μ+L T n.

[0114] It is understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a device for generating RCS simulation data of a moving target. In other embodiments of the present invention, a device for generating RCS simulation data of a moving target may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0115] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0116] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a method for generating moving target RCS simulation data in any embodiment of the present invention is implemented.

[0117] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a method for generating RCS simulation data of a moving target in any embodiment of the present invention.

[0118] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0119] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0120] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0121] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0122] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0123] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical factors in the process, method, article or device including the elements.

[0124] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating RCS simulation data of a moving target, characterized in that: include: Constructing a first covariance matrix of target RCS data samples, wherein the RCS data samples include RCS values ​​of the target at different viewing angles and confidence levels of the RCS values; Based on the target viewing angle and the first covariance matrix, a second covariance matrix is ​​generated; the target viewing angle is related to the motion trajectory of the target to be simulated; Performing Cholesky decomposition on the second covariance matrix to obtain a lower triangular matrix corresponding to the second covariance matrix; Generate the target RCS simulation data based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle; Each viewing angle corresponds to a number of RCS values; The constructing a first covariance matrix of the target RCS data sample includes: Determine the average of a plurality of RCS values ​​corresponding to each viewing angle; Constructing a third covariance matrix of the target RCS data sample based on a plurality of RCS values ​​corresponding to each of the viewing angles and a mean of the plurality of RCS values; Based on the confidence of the RCS value corresponding to each viewing angle, the third covariance matrix is ​​corrected to obtain the first covariance matrix of the target RCS data sample.

2. The method according to claim 1, characterized in that The calculation formula of the third covariance matrix s(θ1, φ1; θ2, φ2) is: Where, θ, φ represent the elevation angle and azimuth angle of the target coordinate system; E is the mathematical expectation, and the superscript * is the complex conjugate; σ0(θ1, φ1) is a random variable, which represents the target RCS value corresponding to the elevation angle θ1 and the azimuth angle φ1; Represents the average RCS value of the target corresponding to the pitch angle θ1 and the azimuth angle φ1; σ0 * (θ2, φ2) is a random variable representing the complex conjugate of the target RCS value corresponding to the pitch angle θ2 and the azimuth angle φ2; It represents the complex conjugate of the average value of the target RCS value corresponding to the elevation angle θ2 and the azimuth angle φ2; The calculation formula of the first covariance matrix ∑(θ1, φ1; θ2, φ2) is: ∑(θ1,φ1;θ2,φ2)=s(θ1,φ1;θ2,φ2) / p c1 / p c2 In the formula, p c1 、p c2 Indicates the confidence level of the RCS values ​​corresponding to the viewing angles (θ1, φ1) and (θ2, φ2).

3. The method according to claim 1, characterized in that The generating a second covariance matrix based on the target viewing angle and the first covariance matrix comprises: An element corresponding to the target viewing angle is selected from the first covariance matrix to generate a second covariance matrix.

4. The method according to claim 1, characterized in that: The second covariance matrix is ​​equal to the product of the lower triangular matrix and the transpose of the lower triangular matrix.

5. The method according to claim 1, characterized in that The generating RCS simulation data of the target based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle includes: Generate a first vector n, wherein the first vector n is composed of the same number of independent and identically distributed Gaussian distributions as the number of the target viewing angles; Generate a second vector μ, wherein the second vector μ is composed of the average of the RCS values ​​corresponding to the target viewing angle; Based on the first vector n, the second vector μ and the transpose L of the lower triangular matrix T , generate RCS simulation data σ0 of the target.

6. The method according to claim 5, characterized in that The RCS simulation data σ0 of the target is generated by the following formula: σ0=μ+L T n.

7. A device for generating RCS simulation data of a moving target, characterized in that: include: A construction module, used to construct a first covariance matrix of target RCS data samples, wherein the RCS data samples include RCS values ​​of the target at different viewing angles and confidence levels of the RCS values; A first generating module, configured to generate a second covariance matrix based on a target viewing angle and the first covariance matrix; the target viewing angle is related to a motion trajectory of the target to be simulated; A decomposition module, used for performing Cholesky decomposition on the second covariance matrix to obtain a lower triangular matrix corresponding to the second covariance matrix; A second generating module is used to generate the target RCS simulation data based on the target viewing angle, the lower triangular matrix and the RCS value corresponding to the target viewing angle; Each viewing angle corresponds to a number of RCS values; The building blocks are used to perform: Determine the average of a plurality of RCS values ​​corresponding to each viewing angle; Constructing a third covariance matrix of the target RCS data sample based on a plurality of RCS values ​​corresponding to each of the viewing angles and a mean of the plurality of RCS values; Based on the confidence of the RCS value corresponding to each viewing angle, the third covariance matrix is ​​corrected to obtain the first covariance matrix of the target RCS data sample.

8. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 6.

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