A method and device for estimating geometric parameters of a ball-cone target, and a storage medium

Abstract

This invention provides a method, apparatus, and storage medium for estimating the geometric parameters of a spherical cone target. The method includes: acquiring sparse echo data of the spherical cone target; determining the value range of the parameters to be estimated based on prior information of the spherical cone target, obtaining a parameter combination set including several parameter combinations; the parameters to be estimated include the base radius and the semi-cone angle; substituting the parameter combinations in the parameter combination set into a geometric diffraction theory model to obtain the RCS data of the spherical cone target; performing two-dimensional imaging and transformation processing on the sparse echo data and the RCS data respectively to obtain a first column vector and a second column vector; constructing a parameter dictionary based on the second column vector; and inputting the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain the estimated target parameter values. The geometric parameter estimation method for spherical cone targets provided by this solution reduces the computational load while ensuring the accuracy of parameter estimation.

Description

Technical Field

[0001] This invention relates to the field of radar technology, and in particular to a method, apparatus, and storage medium for estimating the geometric parameters of a spherical cone target. Background Technology

[0002] Current radar echo data feature extraction typically operates at the data level, estimating the target's scattering center position, amplitude, frequency, and azimuth dependence using a scattering center model. However, when dealing with echo data from real-world dynamic scenarios, the sheer number of parameters leads to a heavy computational burden. Furthermore, for targets with specific shapes, these features fail to intuitively reflect the target's geometric dimensions. In modern radar applications, efficiently recovering the size information of similar-looking targets from sparse echo data will play a crucial role in target identification.

[0003] Currently, when estimating parameters for radar data, the attribute scattering center model is generally chosen to fit the echo data. This model contains seven parameters related to the scattering center. To reduce the size of the dictionary, a group estimation method is generally used, which takes a long time to compute. Furthermore, due to the small amount of data and indistinct features of sparse signals, it is difficult to guarantee the estimation accuracy. Summary of the Invention

[0004] This invention provides a method, apparatus, and storage medium for estimating the geometric parameters of a spherical cone target. This parameter estimation method reduces computational load while ensuring parameter estimation accuracy, and can accurately estimate the geometric dimensions of a fixed-shape target.

[0005] In a first aspect, embodiments of the present invention provide a method for estimating the geometric parameters of a spherical cone target, including:

[0006] Acquire sparse echo data of a spherical cone target;

[0007] Based on the prior information of the spherical cone target, the range of values ​​for the parameters to be estimated is determined, resulting in a parameter combination set including several parameter combinations; wherein, the parameters to be estimated include the bottom radius and the semi-cone angle;

[0008] Substituting the parameter combinations from the parameter combination set into the geometric diffraction theory model, the RCS data of the spherical cone target is obtained;

[0009] The sparse echo data and the RCS data are subjected to two-dimensional imaging and transformation processing respectively to obtain a first column vector and a second column vector; wherein the sparse echo data corresponds to the first column vector;

[0010] Construct a parameter dictionary based on the second column vector;

[0011] The first column vector and the parameter dictionary are input into a preset optimization algorithm for iteration to obtain the target parameter estimate of the parameter to be estimated.

[0012] Optionally, the step of performing two-dimensional imaging and transformation processing on the sparse echo data and the RCS data to obtain a first column vector and a second column vector includes:

[0013] Two-dimensional imaging is performed on the sparse echo data to obtain a first two-dimensional image, and the first two-dimensional image is transformed to obtain the first column vector;

[0014] The RCS data is subjected to two-dimensional imaging to obtain a second two-dimensional image, and the second two-dimensional image is transformed to obtain the second column vector.

[0015] Optionally, the sparse echo data and the RCS data are obtained under the same radar test conditions.

[0016] Optionally, the two-dimensional imaging employs the inverse projection method.

[0017] Optionally, the parameter combination is a numerical combination including the bottom radius and the semi-cone angle.

[0018] Optionally, the step of inputting the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain the target parameter estimate of the parameter to be estimated includes:

[0019] The first column vector and the parameter dictionary are input into a preset optimization algorithm for iteration to obtain an output sparse vector; wherein, the elements in the sparse vector correspond to the weight values ​​of the parameter combination.

[0020] The target parameter estimate is obtained by multiplying the parameter combination set with the sparse vector.

[0021] Optionally, the number of columns in the parameter combination set is the same as the number of rows in the sparse vector.

[0022] Optionally, the step of inputting the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain the output sparse vector includes:

[0023] S1: Initialize the preset optimization algorithm, set the initial iteration number k=1, initialize the residual to the first column vector, initialize the index set to an empty set, and initialize the projection matrix to an empty set;

[0024] S2: For the k-th iteration, the residual r obtained from the (k-1)-th iteration. k-1 Perform an inner product operation with the parameter dictionary to determine the index of the maximum absolute value of the inner product, and update the index set I for the k-th iteration based on the index.k and projection matrix Λ k Wherein, the index set is the set of parameter combinations in the parameter dictionary corresponding to the index, and the projection matrix is ​​the set of atoms in the parameter dictionary corresponding to the index;

[0025] S3: Calculate the projection matrix Λ of the first column vector using the least squares method. k The projection coefficient matrix η is obtained on the projection surface, and the projection coefficient matrix is ​​output; where X is used to represent the first column vector;

[0026] S4: Update residual r k , where r k =X-Λ k η;

[0027] S5: Determine if the number of iterations is less than the preset number of iterations. If so, then k = k + 1 and return to step S2; otherwise, proceed to step S6.

[0028] S6: End the iteration and obtain the sparse vector based on the output projection coefficient matrix.

[0029] Optionally, the preset optimization algorithm is trained using at least two sets of sample sets, each set of sample sets including column vectors and parameter dictionaries as inputs and sparse vectors as outputs.

[0030] Secondly, embodiments of the present invention also provide a device for estimating the geometric parameters of a spherical cone target, comprising:

[0031] The acquisition module is used to acquire sparse echo data of the spherical cone target;

[0032] The parameter determination module is used to determine the value range of the parameters to be estimated based on the prior information of the spherical cone target, and obtain a parameter combination set including several parameter combinations; wherein, the parameters to be estimated include the bottom radius and the semi-cone angle;

[0033] The generation module is used to substitute the parameter combinations in the parameter combination set into the geometric diffraction theory model to obtain the RCS data of the spherical cone target;

[0034] The processing module is used to perform two-dimensional imaging and transformation processing on the sparse echo data and the RCS data respectively to obtain a first column vector and a second column vector; wherein the sparse echo data corresponds to the first column vector;

[0035] The building module is used to construct a parameter dictionary based on the second column vector;

[0036] The iterative operation module is used to input the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain the target parameter estimate of the parameter to be estimated.

[0037] Optionally, the iterative operation module is further configured to input the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain an output sparse vector; and multiply the parameter combination set with the sparse vector to obtain the target parameter estimate; wherein, the elements in the sparse vector all correspond to the weight values ​​of the parameter combination.

[0038] Thirdly, embodiments of the present invention also provide a computing 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 geometric parameter estimation method for the spherical cone target described in any of the above claims.

[0039] 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 geometric parameter estimation method for the spherical cone target described in any of the preceding claims.

[0040] This invention provides a method, apparatus, and storage medium for estimating the geometric parameters of a spherical cone target. The method involves imaging sparse echo data and generating column vectors using two-dimensional image amplitude information. Based on prior information, different values ​​of the parameters to be estimated are set and substituted into the analytical expression of the geometric diffraction theory model of the spherical cone to obtain multiple sets of RCS data, which are then constructed into a parameter dictionary. A preset optimization algorithm is then used to fit the observed echo data of the spherical cone target to estimate the corresponding target parameter values. This invention utilizes the two-dimensional scattering center distribution information of the sparse echo data to determine the convergence direction of the optimization algorithm. Furthermore, considering the completeness of the scattering mechanism description and parameter selection, the geometric diffraction theory model is applied to construct the parameter dictionary. This effectively utilizes prior information about the target's scattering characteristics, obtaining accurate target geometric parameters while conserving computational resources. This solves the problem that previous parameter estimation algorithms were computationally complex and could not directly reflect the physical structure of the spherical cone target. Attached Figure Description

[0041] 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.

[0042] Figure 1 This is a flowchart of a method for estimating the geometric parameters of a spherical cone target according to an embodiment of the present invention;

[0043] Figure 2 This is a model of a spherical cone target provided in one embodiment of the present invention;

[0044] Figure 3 This is a two-dimensional image of sparse echo data provided in one embodiment of the present invention;

[0045] Figure 4 This is a hardware architecture diagram of a computing device provided in an embodiment of the present invention;

[0046] Figure 5 This is a structural diagram of a spherical cone target geometric parameter estimation device provided in an embodiment of the present invention. Detailed Implementation

[0047] 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.

[0048] In existing technologies, when estimating target size parameters based on radar data, conventional attribute scattering center models are often used for fitting. However, this method generates a large amount of redundant calculations, and the estimation results do not intuitively reflect the geometric characteristics of the object. In view of this, this invention proposes a scattering characteristic analysis based on spherical cone targets, using an analytical expression derived from a geometric diffraction theory model, and employing an optimization algorithm to estimate the bottom radius and semi-cone angle parameters in the analytical expression.

[0049] The above is the concept provided by this invention. The specific implementation of the concept provided by this invention will be described below.

[0050] like Figure 1 As shown, this embodiment of the invention provides a method for estimating the geometric parameters of a spherical cone target, the method comprising:

[0051] Step 100: Obtain sparse echo data of the spherical cone target;

[0052] Step 102: Determine the range of values ​​for the parameters to be estimated based on the prior information of the spherical cone target, and obtain a parameter combination set including several parameter combinations; wherein, the parameters to be estimated include the bottom radius and the semi-cone angle;

[0053] Step 104: Substitute the parameter combinations from the parameter combination set into the geometric diffraction theory model to obtain the RCS data of the spherical cone target;

[0054] Step 106: Perform two-dimensional imaging and transformation processing on the sparse echo data and RCS data respectively to obtain the first column vector and the second column vector; wherein, the sparse echo data corresponds to the first column vector;

[0055] Step 108: Construct a parameter dictionary based on the second column vector;

[0056] Step 110: Input the first column vector and parameter dictionary into the preset optimization algorithm for iteration to obtain the target parameter estimate of the parameter to be estimated.

[0057] In this embodiment of the invention, sparse echo data of a spherical cone target is imaged, and a first column vector is generated using two-dimensional image amplitude information. Based on prior information, different values ​​of the parameters to be estimated are set and substituted into the analytical expression of the geometric diffraction theory model of the spherical cone to obtain multiple sets of RCS data, which are then constructed into a parameter dictionary. A preset optimization algorithm is then used to fit the observed echo data of the spherical cone target to obtain the estimated target parameters. This invention, by mining the two-dimensional scattering center distribution information of the sparse echo data, determines the convergence direction of the optimization algorithm. Starting from the completeness of the scattering mechanism description and parameter selection, it applies the geometric diffraction theory model to construct the parameter dictionary, effectively utilizing prior information on the target's scattering characteristics. This obtains accurate target geometric parameters while saving computational resources, thus solving the problem that previous parameter estimation algorithms were computationally complex and could not directly reflect the physical structure of the spherical cone target.

[0058] The following description Figure 1 The execution method of each step is shown.

[0059] First, for step 100, sparse echo data of the spherical cone target is acquired. Specifically, broadband RCS data of the spherical cone target with a sparse distribution and non-uniform azimuth angle is obtained by simulating the observation conditions of the spherical cone target. For example, electromagnetic waves with a bandwidth of 2 GHz are used to acquire data at any 10 observation angles within the range of -3° to 3° relative to the target's rotation axis. Figure 2 The scattered echoes from the spherical cone target shown are sparse echo data. It should be noted that the sparse echo data includes echo data from at least 10 observation angles. This ensures that the parameters to be estimated can be estimated more accurately through sparse echo data, and the more echo data there are in the sparse echo data, the more accurate the estimated value of the parameters to be estimated will be.

[0060] For step 102, the range of values ​​for the parameters to be estimated is determined based on the prior information of the spherical cone target, resulting in a parameter combination set including several parameter combinations; wherein, the parameters to be estimated include the bottom radius and the semi-cone angle, and the parameter combination is a numerical combination including the bottom radius and the semi-cone angle.

[0061] In this invention, the prior information includes the range of values ​​for the bottom radius and the semi-cone angle of the spherical cone target. Based on this range and a preset step size, several parameter combinations, including combinations of the bottom radius and the semi-cone angle, can be further obtained, thus obtaining a parameter combination set. For example, based on the prior information of the spherical cone target, the range of the bottom radius is determined to be 0.1–0.6 m, and the range of the semi-cone angle is determined to be 8°–16°. The step size for the bottom radius is set to 0.05 m, and the step size for the semi-cone angle is set to 0.2°. Then, there are 11 possible values ​​for the bottom radius in the range of 0.1–0.6 m, and 41 possible values ​​for the semi-cone angle in the range of 8°–16°. Therefore, the parameter combination set includes 11*41 parameter combinations.

[0062] In step 104, the parameter combinations in the parameter combination set are substituted into the geometric diffraction theory model to obtain the RCS data of the spherical cone target; wherein, the sparse echo data and RCS data are obtained under the same radar test conditions.

[0063] For example, following the previous example, for each combination of parameters, under the same bandwidth (2GHz) and line-of-sight angle conditions (10 observation angles in the range of -3° to 3°), the RCS data of the spherical cone target is generated analytically using the geometric diffraction theory model.

[0064] In step 106, the sparse echo data and RCS data are subjected to two-dimensional imaging and transformation processing respectively to obtain the first column vector and the second column vector, including:

[0065] Two-dimensional imaging is performed on the sparse echo data to obtain the first two-dimensional image, and the first two-dimensional image is transformed to obtain the first column vector; the sparse echo data corresponds to the first column vector;

[0066] Two-dimensional imaging is performed on the RCS data to obtain a second two-dimensional image, and the second two-dimensional image is transformed to obtain a second column vector;

[0067] Two-dimensional imaging employs the inverse projection method.

[0068] It should be noted that, understandably, the aforementioned two-dimensional imaging can also be achieved using other methods. However, the inverse projection method is preferred, as it allows for more accurate estimations of the target parameters. Continuing with the previous example, the inverse projection method is used to obtain... Figure 2 Two-dimensional imaging was performed on the sparse echo data of the spherical cone target shown, resulting in the following: Figure 3 The image shown is a two-dimensional image of sparse echo data.

[0069] In step 108, a parameter dictionary is constructed based on the second column vector. Specifically, the second two-dimensional image corresponding to each parameter combination is normalized and transformed into a second column vector. Each second column vector is used as an atom, and the atoms corresponding to different parameter combinations are used to construct a parameter dictionary, which includes several columns of second column vectors.

[0070] Before step 110, the method further includes: obtaining a preset optimization algorithm; the preset optimization algorithm is trained by at least two sets of sample sets, each set of sample sets including column vectors and parameter dictionaries as inputs and sparse vectors as outputs.

[0071] It should be noted that the column vectors and parameter dictionary used as input are obtained using the aforementioned method.

[0072] For step 110, the first column vector and parameter dictionary are input into a preset optimization algorithm for iteration to obtain the target parameter estimates of the parameters to be estimated, including:

[0073] The first column vector and the parameter dictionary are input into a preset optimization algorithm for iteration to obtain a sparse vector output; where each element in the sparse vector corresponds to a weight value of the parameter combination.

[0074] S1: Initialize the preset optimization algorithm, set the initial iteration number k=1, initialize the residual to the first column vector, initialize the index set to an empty set, and initialize the projection matrix to an empty set;

[0075] S2: For the k-th iteration, the residual r obtained from the (k-1)-th iteration. k-1 Perform an inner product operation with the parameter dictionary to determine the index of the maximum absolute value of the inner product, and update the index set I for the k-th iteration based on the index. k and projection matrix Λ k The index set is the set of parameter combinations in the parameter dictionary corresponding to the index, and the projection matrix is ​​the set of atoms in the parameter dictionary corresponding to the index.

[0076] S3: Calculate the projection matrix Λ of the first column vector using the least squares method. k The projection coefficient matrix η is obtained on the projection surface, and the projection coefficient matrix is ​​output; where X is used to represent the first column vector;

[0077] S4: Update residual r k , where r k =X-Λ k η;

[0078] S5: Determine if the number of iterations is less than the preset number of iterations. If so, then k = k + 1 and return to step S2; otherwise, proceed to step S6.

[0079] S6: End the iteration and obtain the sparse vector based on the output projection coefficient matrix;

[0080] The target parameter estimate is obtained by multiplying the parameter combination set with the sparse vector.

[0081] Specifically, for the iteration step, initialization is performed first, let r 0 =X, r k I is used to characterize the residual of the k-th iteration. k The set of indices Λ used to represent the k-th iteration k The index set is used to represent the k-th iteration, and X is used to represent the first column vector. The initial iteration number k = 1 is set; then, during the k-th iteration, r is calculated. k Find the index t of the index with the maximum absolute value of the inner product of the inner product and D (parameter dictionary). k and update the index set I k =I k-1 ∪t k Update the projection matrix Among them, dt k Used to characterize index t k The corresponding atom in D is the second column vector; then the least squares method is used to find the first column vector in Λ. k Projection coefficient matrix on Update residual r k =X-Λ k The iteration continues until the termination condition is met, and the output projection coefficient matrix is ​​then sparsed to obtain a sparse vector.

[0082] It should be noted that the projection matrix is ​​the set of atoms corresponding to the maximum index, representing the subspace formed by the atoms closest to the residual in the vector space spanned by the parameter dictionary. Used to represent pseudo-inverse. The preset number of iterations set by the iteration termination condition is obtained by training with at least two sets of samples. In iteration step S3, the projection coefficient matrix can be output, and the projection coefficient matrix can also be directly updated and sparsely transformed to output a sparse vector.

[0083] In this invention, since each element in the sparse vector corresponds to a weight value of a parameter combination, and there is a one-to-one correspondence between elements and parameter combinations, multiplying the parameter combination set with the sparse vector yields the estimated target parameters, namely the bottom radius and the half-cone angle. The inventors have found that this method of calculating the estimated target parameters by multiplying the parameter combination set with the sparse vector significantly reduces the number of iterations. Typically, accurate estimates of the target parameters can be obtained in 3 or 4 iterations with relatively small errors. Continuing with the previous example, when the bottom radius of the spherical cone target ranges from 0.1 to 0.6 m, and the half-cone angle ranges from 8° to 16°, the error between the estimated bottom radius obtained using the geometric parameter estimation method of this invention and the actual bottom radius is ±0.05 m, and the error between the estimated half-cone angle and the actual half-cone angle is ±0.5°.

[0084] In this invention, sparse echo data is imaged and normalized into column vectors. The parameter combination of two parameters to be estimated is then substituted into the analytical expression of the geometric diffraction theory model to generate a dictionary that matches the echo column vectors. An optimization algorithm is then used to linearly combine the atoms in the parameter dictionary and fit them to the echo data, thereby obtaining the estimated target parameters. This invention utilizes the target's own scattering characteristics and employs sparse signals with limited data and features, significantly reducing computational costs and improving identification accuracy. Furthermore, this invention selects only two parameters—the bottom radius and the semi-cone angle—of the spherical cone target. Based on these two key parameters, the geometric dimensions of the spherical cone target can be directly determined. This ensures the completeness of the spherical cone target's size information, further reducing the computational load and improving computational efficiency in the geometric parameter estimation method.

[0085] like Figure 4 , Figure 5 As shown, this embodiment of the invention provides a device for estimating the geometric parameters of a spherical cone target. The device embodiment can be implemented through software, hardware, or a combination of both. From a hardware perspective, as... Figure 4 The diagram shown is a hardware architecture diagram of a computing device housing a spherical cone target geometric parameter estimation device provided in an embodiment of the present invention. (Except for...) Figure 4 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing 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 5 As shown, as a logical device, it is formed by the CPU of its computing device reading the corresponding computer program from the non-volatile memory into memory and running it. This embodiment provides a spherical cone target geometric parameter estimation device, including: an acquisition module 500, a parameter determination module 502, a generation module 504, a processing module 506, a construction module 508, and an iterative calculation module 510;

[0086] The acquisition module 500 is used to acquire sparse echo data of the spherical cone target;

[0087] The parameter determination module 502 is used to determine the range of values ​​of the parameters to be estimated based on the prior information of the spherical cone target, and to obtain a parameter combination set including several parameter combinations; wherein, the parameters to be estimated include the bottom radius and the semi-cone angle;

[0088] The generation module 504 is used to substitute the parameter combinations in the parameter combination set into the geometric diffraction theory model to obtain the RCS data of the spherical cone target;

[0089] Processing module 506 is used to perform two-dimensional imaging and conversion processing on sparse echo data and RCS data respectively to obtain a first column vector and a second column vector; wherein, the sparse echo data corresponds to the first column vector.

[0090] Module 508 is used to construct a parameter dictionary based on the second column vector;

[0091] The iterative operation module 510 is used to input the first column vector and parameter dictionary into a preset optimization algorithm for iteration to obtain the target parameter estimate of the parameter to be estimated.

[0092] In some specific implementations, the acquisition module 500 can be used to perform the above step 100, the parameter determination module 502 can be used to perform the above step 102, the generation module 504 can be used to perform the above step 104, the processing module 506 can be used to perform the above step 106, the construction module 508 can be used to perform the above step 108, and the iterative calculation module 510 can be used to perform the above step 110.

[0093] In some specific implementations, the parameter combination is a numerical combination that includes the bottom radius and the semi-cone angle.

[0094] In some specific implementations, sparse echo data and RCS data are obtained under the same radar test conditions.

[0095] In some specific implementations, the processing module 506 is also used to perform the following operations:

[0096] Two-dimensional imaging is performed on sparse echo data to obtain a first two-dimensional image, and the first two-dimensional image is transformed to obtain a first column vector;

[0097] Two-dimensional imaging is performed on the RCS data to obtain a second two-dimensional image, and the second two-dimensional image is transformed to obtain a second column vector.

[0098] In some specific implementations, two-dimensional imaging employs the inverse projection method.

[0099] In some specific implementations, the iterative operation module 510 is also used to perform the following operations:

[0100] The first column vector and the parameter dictionary are input into a preset optimization algorithm for iteration to obtain a sparse vector output; where each element in the sparse vector corresponds to a weight value of the parameter combination.

[0101] The target parameter estimate is obtained by multiplying the parameter combination set with the sparse vector.

[0102] In some specific implementations, the iterative operation module 510 is also used to perform the following operations:

[0103] S1: Initialize the preset optimization algorithm, set the initial iteration number k=1, initialize the residual to the first column vector, initialize the index set to an empty set, and initialize the projection matrix to an empty set;

[0104] S2: For the k-th iteration, the residual r obtained from the (k-1)-th iteration. k-1 Perform an inner product operation with the parameter dictionary to determine the index of the maximum absolute value of the inner product, and update the index set I for the k-th iteration based on the index. k and projection matrix Λ k The index set is the set of parameter combinations in the parameter dictionary corresponding to the index, and the projection matrix is ​​the set of atoms in the parameter dictionary corresponding to the index.

[0105] S3: Calculate the projection matrix Λ of the first column vector using the least squares method. k The projection coefficient matrix η is obtained on the projection surface, and the projection coefficient matrix is ​​output; where X is used to represent the first column vector;

[0106] S4: Update residual r k , where r k =X-Λ k η;

[0107] S5: Determine if the number of iterations is less than the preset number of iterations. If so, then k = k + 1 and return to step S2; otherwise, proceed to step S6.

[0108] S6: End the iteration and obtain the sparse vector based on the output projection coefficient matrix.

[0109] In some specific embodiments, the spherical cone target geometric parameter estimation device further includes: a training module, which is used to train a preset optimization algorithm through at least two sets of sample sets, each set of sample sets including column vectors and parameter dictionaries as inputs and sparse vectors as outputs.

[0110] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a spherical cone target geometric parameter estimation device. In other embodiments of the present invention, a spherical cone target geometric parameter estimation device 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.

[0111] 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.

[0112] This invention also provides a computing device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for estimating the geometric parameters of a spherical cone target according to any embodiment of this invention.

[0113] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform a method for estimating the geometric parameters of a spherical cone target according to any embodiment of this invention.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 method for estimating the geometric parameters of a spherical cone target, characterized in that, The method comprises the following steps: acquiring sparse echo data of a spherically blunted cone target; determining a value range of an estimated parameter according to prior information of the spherically blunted cone target, to obtain a parameter combination set comprising a plurality of parameter combinations; wherein the estimated parameter comprises a bottom radius and a half cone angle; substituting the parameter combination in the parameter combination set into a geometric diffraction theory model to obtain RCS data of the spherically blunted cone target; respectively performing two-dimensional imaging and conversion processing on the sparse echo data and the RCS data to obtain a first column vector and a second column vector; wherein the sparse echo data corresponds to the first column vector; constructing a parameter dictionary according to the second column vector; inputting the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain a target parameter estimation value of the estimated parameter.

2. The method of claim 1, wherein, The method further comprises the following steps: performing two-dimensional imaging on the sparse echo data to obtain a first two-dimensional image, and performing conversion processing on the first two-dimensional image to obtain the first column vector; performing two-dimensional imaging on the RCS data to obtain a second two-dimensional image, and performing conversion processing on the second two-dimensional image to obtain the second column vector.

3. The method according to claim 1, wherein: the sparse echo data and the RCS data are obtained under the same radar test condition; and / or, the two-dimensional imaging adopts an inverse projection method; and / or, the parameter combination is a numerical combination comprising a bottom radius and a half cone angle.

4. The method of claim 1, wherein, The method further comprises the following steps: inputting the first column vector and the parameter dictionary into the preset optimization algorithm for iteration to obtain an output sparse vector; wherein each element in the sparse vector corresponds to a weight value of a parameter combination; multiplying the parameter combination set and the sparse vector to obtain the target parameter estimation value.

5. The method of claim 4, wherein, The method further comprises the following steps: S1: initializing the preset optimization algorithm, setting an initial iteration number k = 1, initializing a residual as the first column vector, initializing an index set as an empty set, and initializing a projection matrix as an empty set; S2: for the kth iteration, the residual r obtained by the k-1th iteration k-1 determining an index of an inner product absolute value maximum by performing an inner product operation with the parameter dictionary, and updating the index set I obtained by the kth iteration according to the index k and a projection matrix Λ k ; wherein the index set is a set of parameter combinations in the parameter dictionary corresponding to the index, and the projection matrix is a set of atoms in the parameter dictionary corresponding to the index. S3: calculating a projection coefficient matrix η of the first column vector on the projection matrix Λ using a least square method, and outputting the projection coefficient matrix; wherein k X is used to represent the first column vector; X is used to represent the first column vector; S4: update the residual r k where r k = X - A k η; S5: determining whether the iteration number is less than a preset iteration number, if yes, k = k + 1, and returning to step S2; otherwise, executing step S6; S6: ending the iteration, and obtaining the sparse vector according to the output projection coefficient matrix.

6. The method of claim 4, wherein, The preset optimization algorithm is trained by at least two groups of sample sets, each of which comprises a column vector and a parameter dictionary as input and a sparse vector as output.

7. A device for estimating the geometry of a spherically blunted conical target, characterized by The method comprises the following steps: an acquiring module, configured to acquire sparse echo data of a spherically blunted cone target; a parameter determining module, configured to determine a value range of an estimated parameter according to prior information of the spherically blunted cone target, to obtain a parameter combination set comprising a plurality of parameter combinations; wherein the estimated parameter comprises a bottom radius and a half cone angle; The generating module is configured to substitute a parameter combination in the parameter combination set into a geometric diffraction theory model to obtain RCS data of the spherocone target; The processing module is configured to perform two-dimensional imaging and conversion processing on the sparse echo data and the RCS data respectively to obtain a first column vector and a second column vector; wherein the sparse echo data corresponds to the first column vector; The constructing module is configured to construct a parameter dictionary according to the second column vector; The iterative operation module is configured to input the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain a target parameter estimation value of the to-be-estimated parameter.

8. The apparatus of claim 7, wherein The iterative operation module is further configured to input the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain an output sparse vector; and multiply the parameter combination set and the sparse vector to obtain the target parameter estimation value; wherein elements in the sparse vector correspond to weight values of parameter combinations.

9. A computing device, comprising a memory and a processor, the memory storing a computer program, and the processor, when executing the computer program, implements the method of any one of claims 1-6.

10. A computer readable storage medium, storing a computer program, and when the computer program is executed in a computer, the computer program causes the computer to execute the method of any one of claims 1-6.

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