Sparse-aperture with micro-motion component target structured ISAR imaging method and device

Abstract

The application relates to a sparse-aperture target-structured ISAR imaging method and device with a micro-motion component. The method comprises the following steps: first, converting radar echo data of a target with a micro-motion component into a one-dimensional range image; then, according to the relationship between the one-dimensional range image corresponding to the main body and the micro-motion component of the target and the target main body ISAR image to be solved, a problem model is constructed. When solving the target main body ISAR image, the matrix kernel norm, the l1 norm and the l 2,1 norm are used to respectively constrain the low-rank, sparsity and structured sparsity characteristics of the image, so that an optimization model is obtained. Finally, a linear alternating direction multiplier method is used to solve the optimization model, so that the ISAR image of the target main body with the micro-motion component is obtained. The method can improve the quality of the ISAR image of the target main body with the micro-motion component under the condition of a sparse aperture.

Description

Technical Field

[0001] This application relates to the field of inverse synthetic aperture radar imaging technology, and in particular to a sparse aperture target structured ISAR imaging method and apparatus with micro-moving components. Background Technology

[0002] Inverse synthetic aperture radar (ISAR) imaging technology achieves high-resolution imaging of moving targets by utilizing the synthetic aperture of the target relative to a fixed radar platform. However, some complex moving targets often have many micro-moving components added to their structure. Examples include aircraft with propellers, ships with rotating antennas, and satellites. For these targets with micro-moving components, the radar images acquired by ISAR will contain micro-Doppler interference, causing defocusing of the range cells containing these components. Furthermore, in real-world scenarios, the radar echo received is often incomplete due to switching between different channels and other interference factors; this incomplete echo is called a sparse aperture echo. Under sparse aperture conditions, the quality of the radar image will be further degraded by side-lobe interference, severely affecting the interpretation of targets in ISAR images. Under sparse aperture conditions, for targets with micro-moving components, side-lobe interference will superimpose with micro-Doppler interference, making the defocusing of the ISAR image even more severe. Therefore, obtaining high-quality ISAR images of targets with micro-moving components under sparse aperture is a significant challenge.

[0003] Existing ISAR imaging methods often only show some strong scattering points in ISAR images of targets with micro-moving parts obtained under sparse apertures, and cannot reflect the structural features of the target. Summary of the Invention

[0004] Therefore, it is necessary to provide a sparse aperture band micro-moving component target structured ISAR imaging method and device that can effectively improve the ISAR imaging quality, addressing the aforementioned technical problems.

[0005] A sparse aperture band micro-moving component target structured ISAR imaging method, the method comprising:

[0006] Acquire radar echo data of a target with micro-moving components, and convert the radar echo data into a one-dimensional range profile;

[0007] Based on the relationship between the one-dimensional range images of the main body and micro-moving parts in the target and the ISAR image of the target body to be solved, a problem model is constructed.

[0008] When solving the ISAR image of the target subject, the matrix kernel norm, l1 norm, and l are used.2,1 The norms are used to constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model;

[0009] The optimization model is solved using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving components.

[0010] In one embodiment, before constructing the problem model based on the one-dimensional distance image, the one-dimensional distance image is further envelope aligned and phase compensated using the cross-correlation method and the minimum entropy method to achieve translational compensation, thereby obtaining a compensated one-dimensional distance image.

[0011] In one embodiment, the problem model is represented as:

[0012] H = L + S

[0013] L = PX

[0014] In the above formula, L, S, P, and X represent the one-dimensional range image of the main body, the one-dimensional range image of the micro-moving component, the Fourier matrix, and the ISAR image of the target body, respectively.

[0015] In one embodiment, the optimization model is represented as:

[0016] min||L|| * +λS||1+μ||A(X)|| 2，1

[0017] stH=L+S

[0018] L = PX

[0019] In the above formula, ||·|| * The expression represents the constraint of matrix kernel norm on the low-rank property of the one-dimensional range image of the main body, and ||·||1 represents the constraint of matrix l1 norm on the sparsity of the one-dimensional range image of the micro-movement component. 2,1 Indicates the use of l 2,1 The norm constrains the structured sparsity of the ISAR image of the target subject, λ and μ represent regularization parameters, and the operator A(·) represents the conversion of the original image matrix into a standard structured sparse matrix.

[0020] In one embodiment, when the optimization model is solved using the linear alternating direction multiplier method:

[0021] The corresponding augmented Lagrangian function is constructed based on the optimization model.

[0022] The optimization model is transformed into solving multiple corresponding subproblems based on the augmented Lagrange function.

[0023] The multiple sub-problems are solved iteratively in an alternating manner. In each iteration, the one-dimensional range image of the main body, the one-dimensional range image of the micro-moving parts, the ISAR image of the target body, and the augmented Lagrange multipliers are updated according to the augmented Lagrange function.

[0024] In one embodiment, when solving each of the sub-problems alternately and iteratively, the iteration process is controlled by setting the number of iterations or the error precision, so as to obtain the final ISAR image of the target subject;

[0025] When the number of iterations reaches a preset number or the error accuracy reaches a preset value, the target subject ISAR image obtained in the current iteration is the final ISAR image of the target subject with micro-movement components.

[0026] This application also provides a sparse aperture target structured ISAR imaging device with micro-moving components, the device comprising:

[0027] A one-dimensional range image acquisition module is used to acquire radar echo data of targets with micro-moving parts and convert the radar echo data into a one-dimensional range image.

[0028] The problem model construction module is used to construct a problem model based on the relationship between the one-dimensional range images of the main body and micro-moving parts in the target and the ISAR image of the target body that needs to be solved.

[0029] The optimized model building module is used to utilize the matrix kernel norm, l1 norm, and l1 norm when solving the ISAR image of the target subject. 2,1 The norms are used to constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model;

[0030] The optimization model solving module is used to solve the optimization model using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving parts.

[0031] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0032] Acquire radar echo data of a target with micro-moving components, and convert the radar echo data into a one-dimensional range profile;

[0033] Based on the relationship between the one-dimensional range images of the main body and micro-moving parts in the target and the ISAR image of the target body to be solved, a problem model is constructed.

[0034] When solving the ISAR image of the target subject, the matrix kernel norm, l1 norm, and l are used. 2,1The norms are used to constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model;

[0035] The optimization model is solved using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving components.

[0036] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0037] Acquire radar echo data of a target with micro-moving components, and convert the radar echo data into a one-dimensional range profile;

[0038] Based on the relationship between the one-dimensional range images of the main body and micro-moving parts in the target and the ISAR image of the target body to be solved, a problem model is constructed.

[0039] When solving the ISAR image of the target subject, the matrix kernel norm, l1 norm, and l are used. 2,1 The norms are used to constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model;

[0040] The optimization model is solved using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving components.

[0041] The aforementioned sparse aperture structured ISAR imaging method and apparatus for targets with micro-moving components first converts radar echo data of targets with micro-moving components into one-dimensional range images. Then, based on the relationship between the one-dimensional range images corresponding to the main body and micro-moving components in the target and the target main body ISAR image to be solved, a problem model is constructed. When solving the target main body ISAR image, the matrix kernel norm, l1 norm, and l... 2,1 Norms are used to constrain the low-rank, sparse, and structured sparse characteristics of the image, respectively, to obtain an optimized model. Finally, the linear alternating direction multiplier method is used to solve the optimized model, yielding an ISAR image of the target subject with micro-moving components. This method can improve the quality of ISAR images of targets with micro-moving components under sparse aperture conditions. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating a structured ISAR imaging method for targets with sparse aperture bands and micro-moving components in one embodiment.

[0043] Figure 2 This is a flowchart illustrating the iterative updates of multiple sub-problems in one embodiment.

[0044] Figure 3 Here are the experimental results for one example with a 25% sparsity: Figure 3 (a) is the one-dimensional range image of the target. Figure 3 (b) ISAR image of the target subject obtained by the RD method, Figure 3 (c) ISAR image of the target subject obtained by l1-LADMM Figure 3 (d) is the ISAR image of the target subject obtained using this method;

[0045] Figure 4 Here are the experimental results for one example with a 15% sparsity: Figure 4 (a) is the one-dimensional range image of the target. Figure 4 (b) ISAR image of the target subject obtained by the RD method Figure 4 (c) ISAR images of the target subject obtained by l1-LADMM Figure 4 (d) ISAR image of the target subject obtained by this method;

[0046] Figure 5 This is a block diagram of a sparse aperture band micro-movement component target-structured ISAR imaging device in one embodiment.

[0047] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] To address the problem that existing ISAR imaging methods, when used with sparse apertures, often only reveal strong scattering points in ISAR images of targets with micro-moving components, failing to reflect the structural features of the target, this application provides a sparse aperture structured ISAR imaging method for targets with micro-moving components, such as... Figure 1 As shown, it includes the following steps:

[0050] Step S100: Acquire radar echo data of a target with micro-moving components, and convert the radar echo data into a one-dimensional range profile.

[0051] Step S110: Based on the relationship between the one-dimensional range images corresponding to the main body and micro-moving parts in the target and the ISAR image of the target body to be solved, a problem model is constructed.

[0052] Step S120: When solving the ISAR image of the target subject, the matrix kernel norm, l1 norm, and l... 2,1 The norms constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model.

[0053] Step S130: The optimization model is solved using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving components.

[0054] In step S100, the target with micro-movement components can be an aircraft with a propeller, or a ship and satellite with a rotating antenna.

[0055] Before step S110, envelope alignment and phase compensation are required for the one-dimensional range image to achieve translational compensation.

[0056] In this embodiment, the cross-correlation method and the minimum entropy method are used to perform envelope alignment and phase compensation on the one-dimensional range image, respectively.

[0057] In step S110, when constructing the problem model, considering that under sparse aperture conditions, the one-dimensional range image sequence of a target with micro-moving components can be represented in the following matrix form:

[0058] H = L + S (1)

[0059] In formula (1), and These represent the one-dimensional range images of the target, the main body of the target, and the micro-moving components of the target, respectively. Let K be a complex matrix of size K×N, where K and N represent the number of pulses and the number of range cells in the sparse aperture one-dimensional range image sequence, respectively. For sparse aperture data, the number of pulses is less than the number of pulses contained in the full aperture radar echo, i.e., K < M, and the pulse sequence set is a subset of the full aperture pulse sequence set. Where i represents the set of sparse aperture pulse numbers.

[0060] For targets with micro-moving components, acquiring an ISAR image of the main body is the primary objective. Ideally, the ISAR image of the target body and its one-dimensional range profile can be correlated via a Fourier matrix, i.e.:

[0061] L = PX (2)

[0062] In formula (2), ISAR image representing the target subject, This represents a partial Fourier matrix. Assume the complete Fourier matrix is... P is formed by extracting a portion of the row vectors from X. Specifically, the set of indexes of the extracted row vectors is the sparse aperture pulse index set i. Since K < M, Equation (2) is an underdetermined problem with infinitely many solutions.

[0063] Therefore, sparse aperture ISAR imaging of a target with micro-moving components mainly faces two problems. First, it is necessary to separate the one-dimensional range image sequence L of the subject from the one-dimensional range image H of the target. Then, it is necessary to reconstruct the ISAR image X of the target subject from the one-dimensional range image sequence L of the target subject. That is, the signal separation problem shown in Equation (1) and the underdetermined problem shown in Equation (2) need to be solved. Equations (1) and (2) can be used to represent the problem model for solving the ISAR image of the target subject, specifically as follows:

[0064]

[0065] Under sparse aperture conditions, the solutions to the signal separation problem shown in Equation (1) and the underdetermined problem shown in Equation (2) are not unique, and constraints need to be added to obtain a unique solution. For the one-dimensional range profile of the target, the strong correlation between the signals received by the radar in the same range cell within a short observation time results in strong column correlation of the one-dimensional range profile matrix of the target, thus exhibiting low-rank characteristics. For the one-dimensional range profile of the micro-movement component, its energy is mainly distributed in different range cells, resulting in a sparse one-dimensional range profile matrix. For the ISAR image of the target, it is mainly composed of a few interconnected scattering points, thus also exhibiting sparse characteristics. For low-rank and sparse characteristics, the matrix kernel norm and l1 norm can usually be used for constraints to obtain the optimal solution.

[0066] However, for the sparse characteristics of ISAR images of target subjects, the solution obtained by the l1 norm often only reflects the isolated sparse characteristics of each scattering point and cannot consider the correlation between the scattering points. To solve this problem, in step S120, when solving formula (3), i.e., the problem model, l1 norm is introduced. 2,1 The norm constrains it, thereby better preserving the correlation of a block of regions in the image. 2,1 The specific formula for the norm is as follows:

[0067]

[0068] From formula (4), it can be seen that l 2,1 The norm requires not only row sparsity but also column sparsity, which allows the image matrix X to retain its structured sparsity properties.

[0069] Simultaneously, to facilitate subsequent algorithm solutions, the original image matrix needs to be converted into a standard structured sparse matrix using operator A(·). Furthermore, using l... 2,1 Norms constrain standard structured sparse matrices, thereby preserving the structured features of the original image.

[0070] Based on the above analysis, the following optimization model can be obtained:

[0071]

[0072] In formula (5), ||·|| * The expression represents the constraint of matrix kernel norm on the low-rank property of the one-dimensional range image of the main body, and ||·||1 represents the constraint of matrix l1 norm on the sparsity of the one-dimensional range image of the micro-movement component. 2,1 Indicates the use of l 2,1 The norm constrains the structured sparsity of the ISAR image of the target subject, and λ and μ represent regularization parameters, which are used to adjust the weights of each item.

[0073] Next, in step S130, when solving the optimization model using the linear alternating direction multiplier method: first, the corresponding augmented Lagrangian function is constructed based on the optimization model; then, the optimization model is transformed into solving multiple sub-problems based on the augmented Lagrangian function; and finally, the multiple sub-problems are solved iteratively in an alternating manner. During each iteration, the one-dimensional range image of the main body, the one-dimensional range image of the micro-moving component, the ISAR image of the target body, and the augmented Lagrangian multipliers are updated based on the augmented Lagrangian function.

[0074] Specifically, the augmented Lagrangian function constructed according to formula (5) can be expressed as:

[0075]

[0076] In formula (6), Y1 and Y2 represent Lagrange multiplier matrices, and ρ1 and ρ2 represent punishment factors.

[0077] Furthermore, according to the LADMM algorithm (Linearized Alternating Direction Method of Multipliers), equation (6) is transformed into the following sub-problems:

[0078]

[0079] In formula (7), (·) (k) Let represent the variable obtained in the k-th iteration, and η represent the increase factor, which is used to control the upward trend of the punishment factors ρ1 and ρ2.

[0080] Specifically, when iteratively updating formula (7), specific variables are fixed sequentially, and then each other variable is updated and solved. In each iterative update process, the one-dimensional range image L of the main body, the one-dimensional range image S of the micro-moving parts, and the ISAR image X of the target main body are updated sequentially. During the update, formula (7) is substituted into formula (6), and terms irrelevant to the current update variable are omitted, and the corresponding solution method is used for solving. The specific steps are as follows:

[0081] When updating L, substitute formula (7) into formula (6) and omit the following: From the terms that are independent of L, we can obtain:

[0082]

[0083] Equation (8) is the problem of minimizing the nuclear norm, which can be solved using the singular value contraction operator, as follows:

[0084]

[0085] In formula (9), Denotes the singular value contraction factor. Specifically, for any matrix T and any scalar γ, we have:

[0086]

[0087] In formula (10), T=Udiag(σ)V H Let T denote the singular value decomposition of T, where U and V are unitary matrices, σ represents the singular value vector of T, and diag(·) represents a diagonal matrix composed of vectors. Let be a soft threshold operator, for any scalar x and γ, we have Where sgn(·) denotes the sign operator, for any vector x, we have Where, x n This represents the nth element of vector x.

[0088] Next, when updating S, substitute formula (7) into formula (6) and omit... From the terms that are independent of S, we can obtain:

[0089]

[0090] Equation (11) shows the problem of minimizing the l1 norm, which can be solved using a soft threshold operator, as follows:

[0091]

[0092] When updating X, substitute formula (7) into formula (6) and omit... From the terms that are independent of X, we can obtain:

[0093]

[0094] Since there is a partial multiplication of the Fourier matrix P with X, formula (13) is not the standard minimum. 2,1 Norm problems cannot be solved directly using soft threshold operators. Therefore, the quadratic terms in formula (13) To linearize, that is, to linearize the quadratic term in X = X (k) A second-order Taylor expansion is performed at this point, as follows:

[0095]

[0096] In formula (14), G (k) express In X = X (k) The gradient at that point is as follows:

[0097]

[0098] In formula (15), P H Let P represent the conjugate transpose of the partial Fourier matrix P. Then, substituting equation (14) into equation (13) yields:

[0099]

[0100] Formula (16) is to minimize l 2,1 Norm problems can be solved using the following operators:

[0101]

[0102] In formula (17), x1,x2,x n Let A represent the first row, second row, and nth row of matrix X, respectively. * (·) denotes the conjugate operator of operator A(·). Furthermore, for the operator vect-soft... (α) (x), has

[0103] Finally, update Y1, Y2, ρ1, and ρ2 as follows:

[0104] Y1 (k+1) =Y1 (k) +ρ1 (k) (HL (k+1) -S (k+1) (18)

[0105] Y2 (k+1) =Y2 (k) +ρ2 (k) (L(k+1) -PX (k+1) (19)

[0106] ρ1 (k+1) =ηρ1 (k) (20)

[0107] ρ2 (k+1) =ηρ2 (k) (twenty one)

[0108] In this embodiment, when solving each of the above sub-problems alternately and iteratively, the iteration process is controlled by setting the number of iterations or the error precision, so as to obtain the final target subject ISAR image. When the number of iterations reaches a preset number or the error precision reaches a preset value, the target subject ISAR image obtained in the current iteration is the final ISAR image of the target subject with micro-movement components.

[0109] In this paper, experiments were also conducted based on this method to verify its effectiveness.

[0110] like Figure 3 As shown, a one-dimensional range image of the target with a 25% sparsity and ISAR images of the target obtained by different methods are presented. From Figure 3 As can be seen, under the combined effects of side lobe interference and micro-Doppler interference caused by sparse aperture, the ISAR image obtained by the RD method is severely defocused, making it impossible to identify the target from the image. l1-LADMM effectively removes the interference in the image, but the image connectivity is poor, meaning that the image structural features are destroyed. In contrast, the method proposed in this paper can not only effectively remove side lobe interference and micro-Doppler interference, but also maintain the image connectivity, thus better preserving the structural information and features of the image.

[0111] like Figure 4 As shown, a one-dimensional range image of the target with a 15% sparsity and ISAR images of the target obtained by different methods are presented. From Figure 4 As can be seen, the ISAR images obtained by the RD method become more severely defocused as the sparsity decreases. l1-LADMM can still remove interference from the image, but the structural features of the image are more severely damaged. In contrast, the method proposed in this paper can not only effectively remove side lobe interference and micro-Doppler interference, but also retain most of the structural information and features of the image relatively well.

[0112] The experimental results above effectively demonstrate that the method proposed in this paper can effectively eliminate side grating lobes and micro-Doppler interference caused by targets with micro-moving parts under sparse apertures, obtain high-quality ISAR images of the target body, and remain effective even at low sparsity, thus having high engineering application value.

[0113] In the aforementioned sparse aperture structured ISAR imaging method for targets with micro-moving components, the target characteristics are fully constrained using prior target information to obtain an ISAR image of the target body with micro-moving components. The matrix kernel norm and l1 norm are used to constrain the low-rank nature of the one-dimensional range image of the target body and the sparsity of the one-dimensional range image of the micro-moving components; the matrix l1 norm is used to constrain the low-rank nature of the target body's one-dimensional range image and the sparsity of the micro-moving component's one-dimensional range image. 2，1 Norm-constrained structured sparsity of ISAR images of target subjects. This method, under sparse aperture conditions, can effectively remove side lobe interference and micro-Doppler interference while preserving the structural information and features of the ISAR image of the target subject.

[0114] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0115] In one embodiment, such as Figure 5 As shown, a sparse aperture target structured ISAR imaging device with micro-moving components is provided, including: a one-dimensional range image acquisition module 200, a problem model construction module 210, an optimization model construction module 220, and an optimization model solving module 230, wherein:

[0116] The one-dimensional range image acquisition module 200 is used to acquire radar echo data of a target with micro-moving parts and convert the radar echo data into a one-dimensional range image.

[0117] The problem model construction module 210 is used to construct a problem model based on the relationship between the one-dimensional range image corresponding to the main body and micro-moving parts in the target and the ISAR image of the target body to be solved.

[0118] The optimization model construction module 220 is used to utilize the matrix kernel norm, l1 norm, and l1 norm when solving the ISAR image of the target subject. 2,1 The norms are used to constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model;

[0119] The optimization model solving module 230 is used to solve the optimization model using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving parts.

[0120] Specific limitations regarding the sparse aperture ISAR imaging device for targets with micro-moving parts can be found in the limitations of the sparse aperture ISAR imaging method for targets with micro-moving parts described above, and will not be repeated here. Each module in the aforementioned sparse aperture ISAR imaging device for targets with micro-moving parts can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0121] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a sparse aperture target structured ISAR imaging method with micro-moving components. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0122] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0124] Acquire radar echo data of a target with micro-moving components, and convert the radar echo data into a one-dimensional range profile;

[0125] Based on the relationship between the one-dimensional range images of the main body and micro-moving parts in the target and the ISAR image of the target body to be solved, a problem model is constructed.

[0126] When solving the ISAR image of the target subject, the matrix kernel norm, l1 norm, and l are used. 2,1 The norms are used to constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model;

[0127] The optimization model is solved using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving components.

[0128] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0129] Acquire radar echo data of a target with micro-moving components, and convert the radar echo data into a one-dimensional range profile;

[0130] Based on the relationship between the one-dimensional range images of the main body and micro-moving parts in the target and the ISAR image of the target body to be solved, a problem model is constructed.

[0131] When solving the ISAR image of the target subject, the matrix kernel norm, l1 norm, and l are used. 2,1 The norms are used to constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model;

[0132] The optimization model is solved using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving components.

[0133] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0134] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A sparse aperture target structured ISAR imaging method with micro-moving components, characterized in that, The method includes: The radar echo data of the target with micro-moving components is acquired, the radar echo data is converted into a one-dimensional range image, and the one-dimensional range image is envelope aligned and phase compensated by the cross-correlation method and the minimum entropy method to achieve translational compensation, so as to obtain the compensated one-dimensional range image. Based on the relationship between the one-dimensional range images corresponding to the main body and micro-moving parts in the target and the ISAR image of the target body to be solved, a problem model is constructed, which is expressed as: In the above formula, L , S , P , X These represent the one-dimensional range profile of the main body, the one-dimensional range profile of the micro-moving component, the Fourier matrix, and the ISAR image of the target body, respectively. When solving the ISAR image of the target subject, the matrix kernel norm is used. l 1-norm and l 2,1 The norms constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimization model, which is expressed as: In the above formula, This indicates that the matrix nuclear norm is used to constrain the low-rank property of the one-dimensional range image of the subject. Indicates the use of matrices l The L1 norm constrains the sparsity of the one-dimensional distance image of the micro-motion component. Indicates adoption l 2,1 The norm constrains the structured sparsity of the ISAR image of the target subject. , Represents the regularization parameters, operators This represents the conversion of the original image matrix into a standard structured sparse matrix; The optimization model is solved using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving components. When solving the optimization model using the linear alternating direction multiplier method: an augmented Lagrangian function is constructed based on the optimization model; the optimization model is then transformed into solving multiple sub-problems based on the augmented Lagrangian function; these sub-problems are solved iteratively and alternately; and in each iteration, the one-dimensional range image of the subject, the one-dimensional range image of the micro-moving components, the ISAR image of the target subject, and the augmented Lagrangian multipliers are updated based on the augmented Lagrangian function.

2. The sparse aperture band micro-moving component target structured ISAR imaging method according to claim 1, characterized in that, When solving each of the sub-problems alternately and iteratively, the iteration process is controlled by setting the number of iterations or the error precision, so as to obtain the final ISAR image of the target subject. When the number of iterations reaches a preset number or the error accuracy reaches a preset value, the target subject ISAR image obtained in the current iteration is the final ISAR image of the target subject with micro-movement components.

3. A sparse aperture ISAR imaging device with micro-moving components for structured targets, characterized in that, The apparatus implements the sparse aperture band micro-moving component target structured ISAR imaging method according to any one of claims 1 or 2, the apparatus comprising: A one-dimensional range image acquisition module is used to acquire radar echo data of targets with micro-moving parts and convert the radar echo data into a one-dimensional range image. The problem model construction module is used to construct a problem model based on the relationship between the one-dimensional range images of the main body and micro-moving parts in the target and the ISAR image of the target body that needs to be solved. The optimized model building module is used to solve the ISAR image of the target subject by utilizing the matrix kernel norm, l 1-norm and l 2,1 The norms are used to constrain the low-rank, sparse, and structured sparse properties of the image, respectively, to obtain the optimized model; The optimization model solving module is used to solve the optimization model using the linear alternating direction multiplier method to obtain an ISAR image of the target subject with micro-moving parts.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 2.

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