A target parameter estimation method and device based on an intelligent optimization algorithm

Abstract

The application provides a target parameter estimation method and device based on an intelligent optimization algorithm, wherein the method comprises the following steps: establishing a flat-bottomed cone parameterization model to obtain total simulation dynamic characteristic data of a target to be estimated; wherein the flat-bottomed cone parameterization model has three scattering centers; calculating a loss function of a particle swarm algorithm based on the flat-bottomed cone parameterization model; optimizing a basic particle swarm algorithm based on the loss function to obtain a particle swarm optimization algorithm; and processing the total simulation dynamic characteristic data by using the particle swarm optimization algorithm to obtain target parameters of the target to be estimated. The scheme can obtain the required target parameter information without relying on target electromagnetic scattering broadband characteristic data, so as to meet the test requirement.

Description

Technical Field

[0001] This invention relates to the field of radar parameter estimation technology, and in particular to a target parameter estimation method and apparatus based on intelligent optimization algorithms. Background Technology

[0002] Target structural dimensions estimation is a crucial part of target characteristic analysis and identification research. Target structural dimensions obtained from radar measurements can be used for target identification. Parameterized estimation of targets using their dynamic RCS (Radar Cross Section) significantly advances research on target characteristic extraction and identification.

[0003] In related technologies, the methods for extracting target parameter information are relatively simple, relying on broadband electromagnetic scattering characteristic data of the target, which cannot meet the needs of actual experiments.

[0004] Therefore, there is an urgent need for a target parameter estimation method and device based on intelligent optimization algorithms to solve the above-mentioned technical problems. Summary of the Invention

[0005] To meet the needs of actual testing processes, embodiments of the present invention provide a target parameter estimation method and apparatus based on intelligent optimization algorithms.

[0006] In a first aspect, embodiments of the present invention provide a target parameter estimation method based on an intelligent optimization algorithm, comprising:

[0007] A flat-bottomed cone parametric model is established to obtain the total simulated dynamic characteristic data of the target to be estimated; wherein, the flat-bottomed cone parametric model has three scattering centers;

[0008] Based on the flat-bottomed cone parameterized model, the loss function of the particle swarm optimization algorithm is calculated.

[0009] The basic particle swarm optimization algorithm is optimized based on the loss function to obtain the particle swarm optimization algorithm.

[0010] The particle swarm optimization algorithm is used to process the overall simulation dynamic characteristic data to obtain the target parameters of the target to be estimated.

[0011] Preferably, the total simulation dynamic characteristic data is calculated using the following formula:

[0012]

[0013] In the formula, σ represents the overall simulation dynamic characteristic data; i Simulated dynamic characteristic data for each scattering center; ψ i It is an intermediate variable.

[0014] Preferably, the simulated dynamic data of the first scattering center is calculated using the following formula:

[0015]

[0016] n = 1.5 + γ / π

[0017] ψ1=π / 4-2k0asinφ

[0018] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0019] Preferably, the simulated dynamic data of the second scattering center is calculated using the following formula:

[0020]

[0021] n = 1.5 + γ / π

[0022] ψ2=-π / 4+2k0asinφ

[0023] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0024] Preferably, the simulated dynamic data of the third scattering center is calculated using the following formula:

[0025]

[0026] n = 1.5 + γ / π

[0027] ψ3=2k0[(acotγ-b / sinγ)cosφ+b]

[0028] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0029] Preferably, based on the flat-bottomed cone parameterized model, the loss function of the particle swarm optimization algorithm is calculated, including:

[0030] Based on the parameterized model of the flat-bottomed cone target, static characteristic data are obtained;

[0031] The static characteristic data is obtained by performing interpolation processing on the radar line-of-sight angle;

[0032] The loss function is obtained by calculating the error between the total simulated dynamic characteristic data and the measured dynamic characteristic data.

[0033] Preferably, the basic particle swarm optimization algorithm is optimized based on the loss function to obtain a particle swarm optimization algorithm, including:

[0034] Establish a basic particle swarm optimization algorithm model;

[0035] The loss function is input into the basic particle swarm optimization algorithm model to obtain the particle swarm optimization algorithm.

[0036] Secondly, embodiments of the present invention also provide a target parameter estimation device based on an intelligent optimization algorithm, comprising:

[0037] A modeling unit is used to establish a flat-bottomed cone parametric model to obtain the total simulated dynamic characteristic data of the target to be estimated; wherein, the flat-bottomed cone parametric model has three scattering centers;

[0038] The computing unit is used to calculate the loss function of the particle swarm optimization algorithm based on the flat-bottomed cone parameterized model.

[0039] An optimization unit is used to optimize the basic particle swarm algorithm based on the loss function to obtain a particle swarm optimization algorithm.

[0040] The processing unit is used to process the overall simulation dynamic characteristic data using the particle swarm optimization algorithm to obtain the target parameters of the target to be estimated.

[0041] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of this specification.

[0042] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.

[0043] This invention provides a target parameter estimation method and apparatus based on an intelligent optimization algorithm. First, the target's relative radar attitude angle is obtained based on the electromagnetic scattering characteristics and motion characteristics data of the moving target, resulting in a target attitude angle sequence. A parameterized model of a flat-bottomed conical target is constructed, and a loss function for a particle swarm optimization algorithm is designed. After determining the loss function, the particle swarm optimization algorithm architecture is built. Finally, the target size parameters are estimated using the particle swarm optimization algorithm. This method allows the target's parameter information to be obtained independently of the target's broadband electromagnetic scattering characteristics data, meeting testing requirements. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of a target parameter estimation method based on an intelligent optimization algorithm provided in an embodiment of the present invention;

[0046] Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention;

[0047] Figure 3 This is a structural diagram of a target parameter estimation device based on an intelligent optimization algorithm provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the structure of the target to be estimated provided in an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of the iteration curve of the particle swarm optimization algorithm provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0051] Please refer to Figure 1 This invention provides a target parameter estimation method based on an intelligent optimization algorithm, the method comprising:

[0052] Step 100: Establish a flat-bottomed cone parameterized model to obtain the total simulated dynamic characteristic data of the target to be estimated; wherein, the flat-bottomed cone parameterized model has three scattering centers;

[0053] Step 102: Based on the flat-bottomed cone parameterized model, calculate the loss function of the particle swarm optimization algorithm;

[0054] Step 104: Optimize the basic particle swarm algorithm based on the loss function to obtain the particle swarm optimization algorithm;

[0055] Step 106: Process the overall simulation dynamic characteristic data using the particle swarm optimization algorithm to obtain the target parameters of the target to be estimated.

[0056] In this embodiment of the invention, the target's relative radar attitude angle is first obtained based on the electromagnetic scattering characteristic data and motion characteristic data of the moving target, resulting in a target attitude angle sequence. A parameterized model of the flat-bottomed conical target is constructed, and a loss function for the particle swarm optimization algorithm is designed. After determining the loss function of the particle swarm optimization algorithm, its architecture is built. Finally, the target size parameter is estimated using the particle swarm optimization algorithm. This method allows the target's parameter information to be obtained independently of the target's broadband electromagnetic scattering characteristic data, meeting testing requirements.

[0057] The following description Figure 1 The execution method of each step is shown.

[0058] First, for step 100, a flat-bottomed cone parametric model is established to obtain the total simulation dynamic characteristic data of the target to be estimated.

[0059] In this embodiment of the invention, a parametric model is constructed based on the flat-bottomed cone structure (please refer to...). Figure 4 A three-scattering-center model is constructed, with the bottom center selected as the reference center. The total RCS of the target to be estimated is calculated using the following formula:

[0060]

[0061] In the formula, σ represents the overall simulation dynamic characteristic data; i Simulated dynamic characteristic data for each scattering center; ψ i It is an intermediate variable.

[0062] In this embodiment of the invention, the simulated dynamic data of the first scattering center is calculated using the following formula:

[0063]

[0064] n = 1.5 + γ / π

[0065] ψ1=π / 4-2k0asinφ

[0066] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0067] In this embodiment of the invention, the simulated dynamic data of the second scattering center is calculated using the following formula:

[0068]

[0069] n = 1.5 + γ / π

[0070] ψ2=-π / 4+2k0asinφ

[0071] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0072] In this embodiment of the invention, the simulated dynamic data of the third scattering center is calculated using the following formula:

[0073]

[0074] n = 1.5 + γ / π

[0075] ψ3=2k0[(acotγ-b / sinγ)cosφ+b]

[0076] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0077] For step 102, the loss function of the particle swarm optimization algorithm is calculated based on the flat-bottomed cone parameterized model.

[0078] In this embodiment of the invention, the loss function is calculated through the following process:

[0079] First, static characteristic data are obtained based on the parameterized model of the flat-bottomed cone target. Then, the static characteristic data is processed by difference based on the radar line-of-sight angle to obtain the measured dynamic characteristic data. Finally, the error between the total simulated dynamic characteristic data and the measured dynamic characteristic data is calculated to obtain the loss function.

[0080] For step 104, the basic particle swarm algorithm is optimized based on the loss function to obtain the particle swarm optimization algorithm.

[0081] In this embodiment of the invention, the particle swarm optimization algorithm is calculated through the following process: First, a basic particle swarm optimization algorithm model is established; then, the loss function is input into the basic particle swarm optimization algorithm model to obtain the particle swarm optimization algorithm (please refer to...). Figure 5 ).

[0082] like Figure 2 , Figure 3 As shown, this embodiment of the invention provides a target parameter estimation device based on an intelligent optimization algorithm. The device embodiment can be implemented in software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for estimating target parameters based on an intelligent optimization algorithm, provided in an embodiment of the present invention. Except for... Figure 2In 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 packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of its electronic device reading the corresponding computer program from non-volatile memory into memory and running it. This embodiment provides a target parameter estimation device based on an intelligent optimization algorithm, comprising:

[0083] Modeling unit 300 is used to establish a flat-bottomed cone parametric model to obtain the total simulated dynamic characteristic data of the target to be estimated; wherein, the flat-bottomed cone parametric model has three scattering centers;

[0084] The calculation unit 302 is used to calculate the loss function of the particle swarm optimization algorithm based on the flat-bottomed cone parameterized model.

[0085] The optimization unit 304 is used to optimize the basic particle swarm algorithm based on the loss function to obtain a particle swarm optimization algorithm.

[0086] The processing unit 306 is used to process the overall simulation dynamic characteristic data using the particle swarm optimization algorithm to obtain the target parameters of the target to be estimated.

[0087] In this embodiment of the invention, the overall simulation dynamic characteristic data is calculated using the following formula:

[0088]

[0089] In the formula, σ represents the overall simulation dynamic characteristic data; i Simulated dynamic characteristic data for each scattering center; ψ i It is an intermediate variable.

[0090] In this embodiment of the invention, the simulated dynamic data of the first scattering center is calculated using the following formula:

[0091]

[0092] n = 1.5 + γ / π

[0093] ψ1=π / 4-2k0asinφ

[0094] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0095] In this embodiment of the invention, the simulated dynamic data of the second scattering center is calculated using the following formula:

[0096]

[0097] n = 1.5 + γ / π

[0098] ψ2=-π / 4+2k0asinφ

[0099] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0100] In this embodiment of the invention, the simulated dynamic data of the third scattering center is calculated using the following formula:

[0101]

[0102] n = 1.5 + γ / π

[0103] ψ3=2k0[(acotγ-b / sinγ)cosφ+b]

[0104] In the formula, φ is the incident angle of the electromagnetic wave, a is the radius of the bottom surface of the flat-bottomed cone target, b is the radius of the spherical cone, and γ is the cone angle of the spherical cone.

[0105] In this embodiment of the invention, when the calculation unit 302 calculates the loss function of the particle swarm optimization algorithm based on the flat-bottomed cone parameterized model, it specifically performs the following operations: obtaining static characteristic data based on the flat-bottomed cone target parameterized model; performing interpolation processing on the static characteristic data based on the radar line-of-sight angle to obtain measured dynamic characteristic data; and performing error calculation on the total simulated dynamic characteristic data and the measured dynamic characteristic data to obtain the loss function.

[0106] In this embodiment of the invention, when the optimization unit 304 performs optimization of the basic particle swarm optimization algorithm based on the loss function to obtain the particle swarm optimization algorithm, it specifically performs the following operations:

[0107] A basic particle swarm optimization (PSO) algorithm model is established; the loss function is input into the basic PSO algorithm model to obtain the PSO optimization algorithm.

[0108] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a target parameter estimation device based on an intelligent optimization algorithm. In other embodiments of the present invention, a target parameter estimation device based on an intelligent optimization algorithm may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0109] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0110] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a target parameter estimation method based on an intelligent optimization algorithm according to any embodiment of this invention.

[0111] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a target parameter estimation method based on an intelligent optimization algorithm according to any embodiment of this invention.

[0112] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0113] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0114] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0115] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0116] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0117] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0118] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A target parameter estimation method based on intelligent optimization algorithms, characterized in that, include: A flat-bottomed cone parametric model is established to obtain the total simulated dynamic characteristic data of the target to be estimated; wherein, the flat-bottomed cone parametric model has three scattering centers; Based on the flat-bottomed cone parameterized model, the loss function of the particle swarm optimization algorithm is calculated. The basic particle swarm optimization algorithm is optimized based on the loss function to obtain the particle swarm optimization algorithm. The particle swarm optimization algorithm is used to process the overall simulation dynamic characteristic data to obtain the target parameters of the target to be estimated; The overall simulation dynamic characteristic data is calculated using the following formula: In the formula, This is the dynamic characteristic data for the overall simulation; Simulated dynamic characteristic data for each scattering center; As an intermediate variable; The simulated dynamic data for the first scattering center was calculated using the following formula: In the formula, The angle of incidence of the electromagnetic wave. Let b be the radius of the flat-bottomed cone target, and b be the radius of the spherical cone. The cone angle of the ball head cone; The simulated dynamic data for the second scattering center was calculated using the following formula: In the formula, The angle of incidence of the electromagnetic wave. Let b be the radius of the flat-bottomed cone target, and b be the radius of the spherical cone. The cone angle of the ball head cone; The simulated dynamic data for the third scattering center was calculated using the following formula: In the formula, The angle of incidence of the electromagnetic wave. Let b be the radius of the flat-bottomed cone target, and b be the radius of the spherical cone. The cone angle of the ball head cone; Based on the flat-bottomed cone parameterized model, the loss function of the particle swarm optimization algorithm is calculated, including: Based on the parameterized model of the flat-bottomed cone target, static characteristic data are obtained; The static characteristic data is obtained by performing interpolation processing on the radar line-of-sight angle; The loss function is obtained by calculating the error between the total simulated dynamic characteristic data and the measured dynamic characteristic data.

2. The method according to claim 1, characterized in that, The basic particle swarm optimization algorithm is optimized based on the aforementioned loss function to obtain a particle swarm optimization algorithm, including: Establish a basic particle swarm optimization algorithm model; The loss function is input into the basic particle swarm optimization algorithm model to obtain the particle swarm optimization algorithm.

3. A target parameter estimation device based on an intelligent optimization algorithm, characterized in that, The method applied to any one of claims 1-2 includes: A modeling unit is used to establish a flat-bottomed cone parametric model to obtain the total simulated dynamic characteristic data of the target to be estimated; wherein, the flat-bottomed cone parametric model has three scattering centers; The computing unit is used to calculate the loss function of the particle swarm optimization algorithm based on the flat-bottomed cone parameterized model. An optimization unit is used to optimize the basic particle swarm algorithm based on the loss function to obtain a particle swarm optimization algorithm. The processing unit is used to process the overall simulation dynamic characteristic data using the particle swarm optimization algorithm to obtain the target parameters of the target to be estimated.

4. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-2.

5. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-2.

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