Sparse inverse synthetic aperture radar imaging and self-focusing method and device

Abstract

The invention discloses a sparse inverse synthetic aperture radar imaging and self-focusing method and device. The method comprises the following steps: respectively initializing a target image matrix and a phase error diagonal matrix by using a zero matrix and a unit matrix; calculating a kth iteration auxiliary matrix according to the target image matrix, the phase error diagonal matrix and the sparse aperture range profile sequence; determining the sparseness of the (k + 1) th iteration based on the kth iteration auxiliary matrix; calculating an iterative value through an iterative formula, and reconstructing a (k + 1) th iterative solution of the function; and when an iteration termination condition is satisfied, iteration is terminated to obtain an estimation signal, otherwise iteration is continued, through the method, a space target image can be efficiently reconstructed from an echo signal obtained from a sparse aperture without any prior knowledge, the influence of a sub-sampling pulse signal on phase angle estimation is solved, and the estimation precision is improved. The quality of the reconstructed image is improved, the algorithm complexity is reduced, and manual parameter adjustment is avoided.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and more specifically, to a sparse inverse synthetic aperture radar imaging and autofocusing method and device. Background Art

[0002] Implementing automatic focusing and imaging for sparse-aperture inverse synthetic aperture radar (ISAR) involves two key components: first, estimating the phase angle error in the diagonal matrix, and second, reconstructing the target image from a small sequence of range profiles. The first component is addressed using the minimum entropy method, but this approach is complex and its convergence is unproven. For the second component, reconstruction using the alternating multiplier algorithm or soft thresholding iterative algorithm requires manual hyperparameter tuning, limiting their practical application.

[0003] These existing algorithms still have much room for improvement in terms of avoiding manual parameter adjustment, reducing algorithm complexity and improving reconstruction accuracy. Summary of the Invention

[0004] In response to at least one defect or improvement need in the prior art, the present invention provides a sparse inverse synthetic aperture radar imaging and autofocusing method and device, which solves the influence of sub-sampled pulse signals on phase angle estimation, improves the quality of reconstructed images, reduces algorithm complexity and avoids manual parameter adjustment.

[0005] To achieve the above-mentioned object, according to a first aspect of the present invention, a sparse inverse synthetic aperture radar imaging and autofocusing method is provided, comprising: initializing a target image matrix and a phase error diagonal matrix with a zero matrix and a unit matrix, respectively; calculating a k-th iteration auxiliary matrix based on the target image matrix and the phase error diagonal matrix and a sparse aperture range profile sequence; determining the sparsity of the k+1-th iteration based on the k-th iteration auxiliary matrix; calculating an iteration value through an iterative formula and reconstructing the k+1-th iteration solution of a function; and terminating the iteration to obtain an estimated signal if an iteration termination condition is met, otherwise continuing the iteration.

[0006] In an exemplary embodiment, before initializing the target image matrix and the phase error diagonal matrix with a zero matrix and a unit matrix respectively, the method further comprises: determining iteration parameters, the iteration parameters including an iteration step parameter, a scale factor, and an end threshold; inputting a sparse aperture range image sequence H L×N ; Input partial inverse Fourier matrix Φ L×M ; Where L represents the rows of the matrix, N represents the columns of the matrix, and M represents the columns of the matrix.

[0007] In an exemplary embodiment, the k-th iteration auxiliary matrix is ​​calculated according to the target image matrix, the phase error diagonal matrix and the sparse aperture range image sequence, including: the k-th iteration auxiliary matrix F (k) Specifically expressed as, Among them, X (k) is the target image matrix obtained after the kth iteration, α is the maximum eigenvalue, Θ (k) is the phase error diagonal matrix obtained after the kth iteration, H is the sparse aperture range image sequence, and Ψ is the phase function.

[0008] In an exemplary embodiment, determining the sparsity of the k+1th iteration based on the kth iteration auxiliary matrix includes: In the case of , determine the sparsity s of the k+1th iteration (k+1) For (k+1) =s (k) +γ; in In the case of , determine the sparsity s of the k+1th iteration (k+1) For (k+1) =s (k) Among them, F (k) is the auxiliary matrix of the kth iteration, s (k) is the sparsity of the kth iteration, χ is the scale coefficient, and γ is the iteration step parameter.

[0009] In an exemplary embodiment, the iterative formula is used to calculate the iteration value, and the k+1th iteration solution of the reconstruction function includes: calculating the target image matrix obtained after the k+1th iteration for,

[0010]

[0011] Calculate the phase error diagonal matrix Θ obtained after the k+1th iteration (k+1) for,

[0012]

[0013] Among them, k is the number of iterations, i is the i-th row, j is the j-th column, Th (k+1) is the first intermediate parameter, is the auxiliary matrix of the kth iteration, ε (k+1) is the second intermediate parameter, λ (k+1) is the third intermediate parameter, α is the maximum eigenvalue, H is the sparse aperture range image sequence, Ψ is the phase function, L represents the row of the matrix, X (k+1) is the target image matrix obtained after the k+1th iteration.

[0014] In an exemplary embodiment, when the iteration termination condition is satisfied, terminating the iteration to obtain the estimated signal comprises: When the iteration termination condition is met, the estimated signal X is obtained. (k+1) ; Among them, ξ is the end threshold, X (k) is the target image matrix obtained after the kth iteration.

[0015] According to a second aspect of the present invention, a sparse inverse synthetic aperture radar imaging and autofocusing device is also provided, which includes: an initialization unit for initializing a target image matrix and a phase error diagonal matrix with a zero matrix and a unit matrix respectively; a calculation unit for calculating a k-th iteration auxiliary matrix based on the target image matrix and the phase error diagonal matrix and a sparse aperture range image sequence; a first determination unit for determining the sparsity of the k+1-th iteration based on the k-th iteration auxiliary matrix; an iteration unit for calculating an iteration value through an iterative formula and reconstructing the k+1-th iteration solution of a function; and an estimation unit for terminating the iteration to obtain an estimated signal if an iteration termination condition is met, otherwise continuing the iteration.

[0016] According to a third aspect of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned sparse inverse synthetic aperture radar imaging and autofocusing method when run.

[0017] According to a fourth aspect of the present invention, an electronic device is also provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the sparse inverse synthetic aperture radar imaging and autofocusing method through the computer program.

[0018] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0019] The present invention provides a sparse inverse synthetic aperture radar imaging and autofocusing method, which is an algorithm for synchronously completing phase error estimation and inverse synthetic aperture radar imaging (ISAR) image reconstruction of air and space targets under the condition that a sparse aperture high-resolution range image sequence and a partial inverse Fourier matrix are known. The target image and the phase error matrix are initialized with a zero matrix and a unit matrix respectively; the iteration value is calculated by an iterative formula, and the iterative solution of the reconstruction function and the regularization parameter are reconstructed; when the iteration termination condition is met, the estimated signal is obtained, otherwise the iteration is continued. Without any prior knowledge, the space target image can be efficiently reconstructed from the echo signal obtained by the sparse aperture, which not only solves the influence of the sub-sampling pulse signal on the phase angle estimation, but also effectively reduces the algorithm complexity and avoids manual parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A schematic flow chart of an optional sparse inverse synthetic aperture radar imaging and autofocusing method provided in an embodiment of the present application;

[0022] Figure 2 A schematic flow chart of another optional sparse inverse synthetic aperture radar imaging and autofocusing method provided in an embodiment of the present application;

[0023] Figure 3 A schematic structural diagram of an optional sparse inverse synthetic aperture radar imaging and autofocusing device provided in an embodiment of the present application;

[0024] Figure 4 A schematic structural diagram of an optional electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0026] The terms "first," "second," "third," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0027] According to one aspect of the embodiments of the present application, a sparse inverse synthetic aperture radar imaging and autofocusing method is provided. Figure 1 The sparse inverse synthetic aperture radar imaging and autofocusing method provided in the embodiments of the present application is described.

[0028] Figure 1 This is a flow chart of an optional sparse inverse synthetic aperture radar imaging and autofocusing method provided in an embodiment of the present application, such as Figure 1 As shown, the process of the method may include the following steps:

[0029] S102, initializing the target image matrix and the phase error diagonal matrix with a zero matrix and a unit matrix respectively;

[0030] S104, calculating the kth iteration auxiliary matrix according to the target image matrix, the phase error diagonal matrix, and the sparse aperture range image sequence;

[0031] S106, determining the sparsity of the k+1th iteration based on the kth iteration auxiliary matrix;

[0032] S108, calculating the iteration value through the iterative formula, and reconstructing the k+1th iteration solution of the function;

[0033] S110 , when the iteration termination condition is met, terminate the iteration to obtain the estimated signal; otherwise, continue the iteration.

[0034] The sparse inverse synthetic aperture radar imaging and autofocusing method provided in the embodiments of the present application can be applied to scenarios where images of space targets are reconstructed from echo signals obtained from a sparse aperture.

[0035] Figure 2 A flow chart of another optional sparse inverse synthetic aperture radar imaging and autofocusing method provided in the embodiment of the present application, combined with Figure 1 and Figure 2 As shown, under the condition that the sparse aperture high-resolution range image sequence and part of the inverse Fourier matrix are known, phase error estimation and air-space target ISAR image reconstruction can be completed simultaneously. Optionally, the method includes the following steps:

[0036] Step 1: Input sparse aperture high-resolution range image sequence H L×N and the partial inverse Fourier matrix Φ L×M , and various iteration parameters are initialized.

[0037] Step 2: Calculate the auxiliary matrix F for the kth iteration (k) .

[0038] Step 3: Determine the sparsity s of the k+1th iteration (k+1) .

[0039] Step 4: Calculate the intermediate parameter, λ (k+1) , ε (k+1) Th (k+1) .

[0040] Step 5: Calculate the target image matrix X after the k+1th iteration (k+1) , the phase error diagonal matrix Θ obtained after the k+1th iteration (k+1) .

[0041] Step 6, determine X (k+1) Whether the iteration termination condition is met, if it is met, the estimated signal is obtained, otherwise the iteration continues.

[0042] Through the above steps S102 to S110, the target image matrix and the phase error diagonal matrix are initialized with a zero matrix and a unit matrix respectively; the k-th iteration auxiliary matrix is ​​calculated according to the target image matrix and the phase error diagonal matrix and the sparse aperture range image sequence; the sparsity of the k+1-th iteration is determined based on the k-th iteration auxiliary matrix; the iteration value is calculated through the iterative formula, and the k+1-th iteration solution of the function is reconstructed; when the iteration termination condition is met, the iteration is terminated to obtain the estimated signal, otherwise the iteration is continued. Through this method, without any prior knowledge, the spatial target image can be efficiently reconstructed from the echo signal obtained from the sparse aperture, the influence of the sub-sampling pulse signal on the phase angle estimation is solved, the reconstructed image quality is improved, the algorithm complexity is reduced and manual parameter adjustment is avoided.

[0043] In an exemplary embodiment, before initializing the target image matrix and the phase error diagonal matrix with a zero matrix and a unit matrix respectively, the method further includes:

[0044] S11, determining iteration parameters, wherein the iteration parameters include an iteration step parameter, a proportional coefficient, and an end threshold;

[0045] S12, input sparse aperture range image sequence H L×N ;

[0046] S13, input partial inverse Fourier matrix ΦL×M ;

[0047] Among them, L represents the rows of the matrix, N represents the columns of the matrix, and M represents the columns of the matrix.

[0048] In the embodiment of the present application, after inputting the matrix and determining the iteration parameters, the target image and the phase error matrix can be initialized by the zero matrix and the identity matrix respectively, and the target image X (0) =0 M×N , the phase error diagonal matrix Θ (0) =I.

[0049] Furthermore, the hyperparameters are initialized, including: β∈(0,1), τ=g(β), non-negative minimum δ=10 -3 , the maximum eigenvalue α=max(eigΨ H Ψ) + δ, the number of iterations k = 0; where τ = g(β) is based on the formula (2chτ-β) 2 -e -2τ -2τ+2logβ=0, is the hyperbolic cosine function.

[0050] Through this embodiment, when the target image is initialized to a zero matrix, it is easier to accumulate new calculation results into the target image during the iteration process, facilitating subsequent iterative update operations. At the same time, using the identity matrix to initialize the phase error matrix can simplify the initial phase error estimation process.

[0051] In an exemplary embodiment, calculating the k-th iterative auxiliary matrix according to the target image matrix, the phase error diagonal matrix, and the sparse aperture range image sequence includes:

[0052] S21, the k-th iteration auxiliary matrix F (k) Specifically expressed as,

[0053]

[0054] Among them, X (k) is the target image matrix obtained after the kth iteration, α is the maximum eigenvalue, Θ (k) is the phase error diagonal matrix obtained after the kth iteration, H is the sparse aperture range image sequence, and Ψ is the phase function.

[0055] In an exemplary embodiment, determining the sparsity of the k+1th iteration based on the kth iteration auxiliary matrix includes:

[0056] S31, in In the case of , determine the sparsity s of the k+1th iteration (k+1) For (k+1)=s (k) +γ;

[0057] S32, in In the case of , determine the sparsity s of the k+1th iteration (k+1) For (k+1) =s (k) ;

[0058] Among them, F (k) is the auxiliary matrix of the kth iteration, s (k) is the sparsity of the kth iteration, χ is the scale coefficient, and γ is the iteration step parameter.

[0059] In the embodiment of the present application, when determining the sparsity s of the k+1th iteration, (k+1) Afterwards, we can use the sparsity s (k+1) Calculate the intermediate parameter, that is, the first intermediate parameter Th (k+1) , the second intermediate parameter ε (k+1) and the third intermediate parameter λ (k+1) .

[0060] Optionally, the above intermediate parameters are calculated according to the following formulas:

[0061]

[0062] Among them, α is the maximum eigenvalue, β is the hyperparameter, τ=g(β), F (k) is the auxiliary matrix of the kth iteration, and the sparsity s of the k+1th iteration (k+1) .

[0063] Through this embodiment, the imaging resolution can be improved by calculating the sparsity, and adaptive focusing can be achieved.

[0064] In an exemplary embodiment, calculating the iteration value by the iterative formula and reconstructing the k+1th iteration solution of the function includes:

[0065] S41, calculate the target image matrix obtained after the k+1th iteration for,

[0066]

[0067] S42, calculate the phase error diagonal matrix Θ obtained after the k+1th iteration (k+1) for,

[0068]

[0069] Among them, k is the number of iterations, i is the i-th row, j is the j-th column, Th (k+1) is the first intermediate parameter, is the auxiliary matrix of the kth iteration, ε(k+1) is the second intermediate parameter, λ (k+1) is the third intermediate parameter, α is the maximum eigenvalue, H is the sparse aperture range image sequence, Ψ is the phase function, L represents the row of the matrix, X (k+1) is the target image matrix obtained after the k+1th iteration.

[0070] In an exemplary embodiment, when an iteration termination condition is satisfied, terminating the iteration to obtain the estimated signal includes:

[0071] S51, in When the iteration termination condition is met, the estimated signal X is obtained. (k+1) ;

[0072] Among them, ξ is the end threshold, X (k) is the target image matrix obtained after the kth iteration.

[0073] In the embodiment of the present application, when X is calculated, (k+1) and Θ (k+1) After that, determine whether the iteration termination condition is met, that is, whether If it is satisfied, the current estimated signal is obtained and the result is finally output, which is the target image matrix X obtained after the k+1th iteration. (k+1) , the phase error diagonal matrix Θ obtained after the k+1th iteration (k+1) .

[0074] Through this embodiment, an ISAR image is reconstructed from a high-resolution range image sequence received through a sparse aperture, thereby improving the convergence speed and reconstruction accuracy.

[0075] According to another aspect of an embodiment of the present application, a sparse inverse synthetic aperture radar imaging and autofocusing device for implementing the above-mentioned sparse inverse synthetic aperture radar imaging and autofocusing method is also provided. Figure 3 is a schematic structural diagram of an optional sparse inverse synthetic aperture radar imaging and self-focusing device according to an embodiment of the present application, such as Figure 3 As shown, the device may include:

[0076] An initialization unit 302 is configured to initialize the target image matrix and the phase error diagonal matrix with a zero matrix and a unit matrix respectively;

[0077] A calculation unit 304 is configured to calculate a k-th iteration auxiliary matrix according to the target image matrix, the phase error diagonal matrix, and the sparse aperture range image sequence;

[0078] A first determining unit 306 is configured to determine the sparsity of the k+1th iteration based on the kth iteration auxiliary matrix;

[0079] Iteration unit 308, used to calculate the iteration value through the iterative formula and reconstruct the k+1th iteration solution of the function;

[0080] The estimation unit 310 is configured to terminate the iteration to obtain the estimated signal if an iteration termination condition is met, and continue the iteration otherwise.

[0081] It should be noted that the initialization unit 302 in this embodiment can be used to execute the above-mentioned step S102, the calculation unit 304 in this embodiment can be used to execute the above-mentioned step S104, the first determination unit 306 in this embodiment can be used to execute the above-mentioned step S106, the iteration unit 308 in this embodiment can be used to execute the above-mentioned step S108, and the estimation unit 310 in this embodiment can be used to execute the above-mentioned step S110.

[0082] Through the above module, the target image matrix and the phase error diagonal matrix are initialized with a zero matrix and a unit matrix respectively; the k-th iteration auxiliary matrix is ​​calculated based on the target image matrix and the phase error diagonal matrix and the sparse aperture range image sequence; the sparsity of the k+1-th iteration is determined based on the k-th iteration auxiliary matrix; the iteration value is calculated through the iterative formula, and the k+1-th iteration solution of the function is reconstructed; when the iteration termination condition is met, the iteration is terminated to obtain the estimated signal, otherwise the iteration is continued. Through this method, without any prior knowledge, the spatial target image can be efficiently reconstructed from the echo signal obtained from the sparse aperture, the influence of the sub-sampling pulse signal on the phase angle estimation is solved, the reconstructed image quality is improved, the algorithm complexity is reduced and manual parameter adjustment is avoided.

[0083] In an exemplary embodiment, the apparatus further comprises: a second determining unit for determining iteration parameters, wherein the iteration parameters include an iteration step parameter, a proportional coefficient, and an end threshold; a first input unit for inputting a sparse aperture range image sequence H L×N ; The second input unit is used to input the partial inverse Fourier matrix Φ L×M ; Where L represents the rows of the matrix, N represents the columns of the matrix, and M represents the columns of the matrix.

[0084] It should be noted here that the examples and scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments. It should be noted that the above modules as part of the device can run in a hardware environment, can be implemented by software, and can also be implemented by hardware, where the hardware environment includes a network environment.

[0085] According to another aspect of the embodiments of the present application, a storage medium is further provided. Optionally, in this embodiment, the storage medium can be used to execute the program code of the sparse inverse synthetic aperture radar imaging and autofocusing method in the embodiments of the present application.

[0086] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps:

[0087] S1, initialize the target image matrix and phase error diagonal matrix with zero matrix and identity matrix respectively;

[0088] S2, calculating the kth iterative auxiliary matrix according to the target image matrix, the phase error diagonal matrix and the sparse aperture range image sequence;

[0089] S3, determining the sparsity of the k+1th iteration based on the kth iteration auxiliary matrix;

[0090] S4, calculate the iteration value through the iterative formula and reconstruct the k+1th iteration solution of the function;

[0091] S5, when the iteration termination condition is met, terminate the iteration to obtain the estimated signal, otherwise continue the iteration.

[0092] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, which will not be described in detail in this embodiment.

[0093] Among them, computer-readable storage media may include, but are not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0094] According to another aspect of an embodiment of the present application, an electronic device for implementing the above-mentioned sparse inverse synthetic aperture radar imaging and self-focusing method is also provided. The electronic device can be a server, a terminal, or a combination thereof.

[0095] Figure 4 is a schematic structural diagram of an optional electronic device according to an embodiment of the present application, such as Figure 4 As shown, it includes a processor 402, a communication interface 404, a memory 406 and a communication bus 408, wherein the processor 402, the communication interface 404, and the memory 406 communicate with each other via the communication bus 408, wherein,

[0096] Memory 406, for storing computer programs;

[0097] The processor 402 is configured to execute the computer program stored in the memory 406 to implement the following steps:

[0098] S1, initialize the target image matrix and phase error diagonal matrix with zero matrix and identity matrix respectively;

[0099] S2, calculating the kth iterative auxiliary matrix according to the target image matrix, the phase error diagonal matrix and the sparse aperture range image sequence;

[0100] S3, determining the sparsity of the k+1th iteration based on the kth iteration auxiliary matrix;

[0101] S4, calculate the iteration value through the iterative formula and reconstruct the k+1th iteration solution of the function;

[0102] S5, when the iteration termination condition is met, terminate the iteration to obtain the estimated signal, otherwise continue the iteration.

[0103] Optionally, the communication bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The communication interface is used for communication between the electronic device and other devices.

[0104] The memory may include RAM, or may include non-volatile memory, such as at least one disk memory. Alternatively, the memory may also be at least one storage device located away from the aforementioned processor.

[0105] As an example, the memory 406 may include, but is not limited to, the initialization unit 302, the calculation unit 304, the first determination unit 306, the iteration unit 308, and the estimation unit 310 in the sparse inverse synthetic aperture radar imaging and autofocusing device. Furthermore, the memory 406 may also include, but is not limited to, other module units in the sparse inverse synthetic aperture radar imaging and autofocusing device, which will not be described in detail in this example.

[0106] The above-mentioned processor can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processing), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0107] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0108] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0109] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0111] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0112] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0114] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0115] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0116] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0117] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A sparse inverse synthetic aperture radar imaging and autofocusing method, characterized in that: include: Initialize the target image matrix and phase error diagonal matrix with zero matrix and identity matrix respectively; Calculate the kth iteration auxiliary matrix according to the target image matrix, the phase error diagonal matrix and the sparse aperture range image sequence; Determining the sparsity of the k+1th iteration based on the kth iteration auxiliary matrix; Calculate the iteration value through the iterative formula and reconstruct the k+1th iteration solution of the function; If the iteration termination condition is met, the iteration is terminated to obtain the estimated signal; otherwise, the iteration is continued.

2. The sparse inverse synthetic aperture radar imaging and autofocusing method according to claim 1, wherein: Before respectively initializing the target image matrix and the phase error diagonal matrix with a zero matrix and a unit matrix, the method further includes: Determining iteration parameters, wherein the iteration parameters include an iteration step parameter, a proportional coefficient, and an end threshold; Input sparse aperture range image sequence H L×N ; Input partial inverse Fourier matrix Φ L×M ; Among them, L represents the rows of the matrix, N represents the columns of the matrix, and M represents the columns of the matrix.

3. The sparse inverse synthetic aperture radar imaging and autofocusing method according to claim 1, wherein: Calculating the k-th iterative auxiliary matrix according to the target image matrix, the phase error diagonal matrix, and the sparse aperture range image sequence includes: The k-th iteration auxiliary matrix F (k) Specifically expressed as, Among them, X (k) is the target image matrix obtained after the kth iteration, α is the maximum eigenvalue, Θ (k) is the phase error diagonal matrix obtained after the kth iteration, H is the sparse aperture range image sequence, and Ψ is the phase function.

4. The sparse inverse synthetic aperture radar imaging and autofocusing method according to claim 1, wherein: The determining of the sparsity of the k+1th iteration based on the kth iteration auxiliary matrix includes: exist In the case of , determine the sparsity s of the k+1th iteration (k+1) For (k+1) =s (k) +γ; exist In the case of , determine the sparsity s of the k+1th iteration (k+1) For (k+1) =s (k) ; Among them, F (k) is the auxiliary matrix of the kth iteration, s (k) is the sparsity of the kth iteration, χ is the scale coefficient, and γ is the iteration step parameter.

5. The sparse inverse synthetic aperture radar imaging and autofocusing method according to claim 1, wherein: The iterative value is calculated by the iterative formula, and the k+1th iterative solution of the reconstruction function includes: Calculate the target image matrix after the k+1th iteration for, Calculate the phase error diagonal matrix Θ obtained after the k+1th iteration (k+1) for, Among them, k is the number of iterations, i is the i-th row, j is the j-th column, Th (k+1) is the first intermediate parameter, is the auxiliary matrix of the kth iteration, ε (k+1) is the second intermediate parameter, λ (k+1) is the third intermediate parameter, α is the maximum eigenvalue, H is the sparse aperture range image sequence, Ψ is the phase function, L represents the row of the matrix, X (k+1) is the target image matrix obtained after the k+1th iteration.

6. The sparse inverse synthetic aperture radar imaging and autofocusing method according to claim 1, wherein: When the iteration termination condition is met, terminating the iteration to obtain the estimated signal includes: exist When the iteration termination condition is met, the estimated signal X is obtained. (k+1) ; Among them, ξ is the end threshold, X (k) is the target image matrix obtained after the kth iteration.

7. A sparse inverse synthetic aperture radar imaging and self-focusing device, characterized in that: include: an initialization unit, for initializing the target image matrix and the phase error diagonal matrix with a zero matrix and a unit matrix respectively; a calculation unit, configured to calculate a k-th iterative auxiliary matrix according to the target image matrix, the phase error diagonal matrix, and the sparse aperture range image sequence; A first determining unit is configured to determine the sparsity of a k+1th iteration based on the kth iteration auxiliary matrix; Iteration unit, used to calculate the iteration value through the iterative formula and reconstruct the k+1th iteration solution of the function; The estimation unit is used to terminate the iteration and obtain the estimated signal when the iteration termination condition is met, and continue the iteration otherwise.

8. The sparse inverse synthetic aperture radar imaging and autofocusing device according to claim 7, wherein: The device further comprises: A second determining unit is used to determine iteration parameters, wherein the iteration parameters include an iteration step parameter, a proportional coefficient, and an end threshold; The first input unit is used to input the sparse aperture range image sequence H L×N ; The second input unit is used to input the partial inverse Fourier matrix Φ L×M ; Among them, L represents the rows of the matrix, N represents the columns of the matrix, and M represents the columns of the matrix.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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