Based on L 1 / 2 Regularized synthetic aperture radar imaging methods, systems, and storage media

Abstract

The application provides a L 1 / 2 A regularized synthetic aperture radar imaging method, system and storage medium. The method comprises: obtaining echo data of sampling points; decomposing an imaging operator based on a wave number domain imaging algorithm into a phase correction operator and an azimuth correction operator; using a fast solution L 1 / 2 The regularized threshold iteration algorithm processes the echo data to obtain a first intermediate quantity; the first intermediate quantity is used as an operator for iterative calculation until a preset condition is met, and a synthetic aperture radar imaging model is obtained. The embodiment of the application uses an approximate observation operator instead of matrix multiplication based on an approximate observation sparse imaging method, constructs a new sparse autofocusing model, greatly reduces the dimension of the observation matrix, and improves the reconstruction efficiency and imaging quality.

Description

Technical Field

[0001] This application relates to the field of radar two-dimensional imaging technology, and particularly to a method based on L... 1 / 2 Regularized synthetic aperture radar imaging methods, systems, and storage media. Background Technology

[0002] Synthetic Aperture Radar (SAR), a type of microwave remote sensing imaging radar, boasts high azimuth resolution and all-weather, all-day, and long-range observation capabilities, leading to its widespread application. However, radar is susceptible to interference from various factors during actual operation, resulting in missing sampling data, decreased signal-to-noise ratio, and consequently, imaging quality that fails to meet practical requirements. Generally, signal sampling must satisfy the Nyquist sampling theorem. As the amount of information contained in SAR echo data gradually increases, the amount of echo data obtained through traditional sampling methods also gradually increases, placing higher demands on the storage and computation of echo data.

[0003] Some technical solutions employ compressed sensing (CS) technology, which enables sparse signal reconstruction with fewer samples when the signal is sparse. However, when using sparse reconstruction algorithms for SAR image reconstruction, the echo data needs to be transformed from a two-dimensional matrix into a one-dimensional vector. When processing a large number of echo data samples or when the spatial scale of the imaging scene is large, the data consumes a lot of memory and the imaging processing time is long. Therefore, this imaging method has high computational complexity and poor real-time performance, making it unsuitable for real-time imaging of large scenes. Summary of the Invention

[0004] This application is made in view of at least one of the aforementioned technical problems existing in the prior art. According to one aspect of this application, a method based on L... 1 / 2 A regularized synthetic aperture radar imaging method, the method comprising: Acquire echo data from the sampling points; The imaging operator based on the wavenumber domain imaging algorithm is decomposed into a phase correction operator and an azimuth correction operator; Using the phase correction operator and the azimuth correction operator, a fast solution for L is obtained. 1 / 2 A regularized threshold iteration algorithm is used to process the echo data to obtain a first intermediate quantity; The first intermediate quantity is used as an operator for iterative calculation until the preset conditions are met, thus obtaining the synthetic aperture radar imaging model.

[0005] In some embodiments, the azimuth correction operator is obtained by decomposing the imaging operator based on the wavenumber domain imaging algorithm, including: using the imaging operator based on the wavenumber domain imaging algorithm to perform an inverse Fourier transform on the two-dimensional echo data to obtain the azimuth correction operator.

[0006] In some embodiments, the first intermediate quantity is represented as follows: ; in, R i+1 Indicates the first i The auxiliary variable for the +1 iteration is used to update the... i +1 scene scattering coefficient; H λ，μ，1 / 2 Indicates the threshold operator; R i Indicates the first i The auxiliary variable for the second iteration is used to update the third iteration. i Scattering coefficients for each scene; L The undersampling matrix represents the echo data; y Represents the echo data vector; F a This represents the azimuth-to-Fourier transform operator; Oh This represents the phase correction operator; F This represents the phase error matrix.

[0007] In some embodiments, the first intermediate quantity is used as an operator for iterative calculation, wherein the calculation formula is as follows: ; in, x Represents the scene scattering coefficient; R i+1 Indicates the first i The auxiliary variable for the +1 iteration is used to update the... i +1 scene scattering coefficient; R i Indicates the first i The auxiliary variable for the second iteration is used to update the third iteration. i Scattering coefficients for each scene; t i Indicates the first i The time step of each iteration; t i+1 Indicates the first i +1 iteration time step.

[0008] In some embodiments, the method further includes: iteratively calculating the first intermediate quantity using the phase correction operator to obtain the target phase of the aperture radar imaging model.

[0009] In some embodiments, the method further includes: Initialization processing is performed based on the aforementioned wavenumber domain imaging algorithm; The initialization process includes setting parameters based on the wavenumber domain imaging algorithm.

[0010] In some embodiments, the imaging operator is represented as follows: ; in, Y This represents two-dimensional synthetic aperture radar echo data. F ɑ This indicates a Fourier transform of the orientation. F r This represents a distance-to-Fourier transform. F a -1 This indicates the inverse Fourier transform of the orientation; F r -1 This represents the inverse Fourier transform of distance; i ref This represents the uniform distance migration correction matrix. S (·) denotes the Stolt interpolation operator.

[0011] In some embodiments, the preset conditions include: ; in, x Represents the scene scattering coefficient; e This indicates the preset threshold value.

[0012] Another aspect of this application embodiment provides a L-based 1 / 2 A regularized synthetic aperture radar imaging system, the system comprising: A memory and a processor, wherein the memory stores a computer program that is executed by the processor, and the computer program, when executed by the processor, causes the processor to perform the L-based operation as described above. 1 / 2 A regularized synthetic aperture radar imaging method.

[0013] In another aspect, this application provides a storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the L-based operation as described above. 1 / 2 A regularized synthetic aperture radar imaging method.

[0014] The embodiments of this application are based on L 1 / 2The regularized synthetic aperture radar (SAR) imaging method decomposes the imaging operator based on the wavenumber domain imaging algorithm into a phase correction operator and an azimuth correction operator. A first intermediate quantity is obtained through the phase correction operator and the azimuth correction operator. Then, the first intermediate quantity is used as an operator for iterative calculation to obtain the SAR imaging model. The embodiments of this application are based on a sparse imaging method of approximate observation. Approximate observation operators are used to replace matrix multiplication to construct a new sparse self-focusing model, which greatly reduces the dimension of the observation matrix and improves reconstruction efficiency and imaging quality. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 The following is an illustration based on L according to an embodiment of this application. 1 / 2 A schematic flowchart of a regularized synthetic aperture radar imaging method; Figure 2 A schematic diagram of SAR imaging simulation parameters set in the initialization step according to an embodiment of this application is shown. Figure 3(a) illustrates a wavenumber domain imaging algorithm using conventional techniques ( ω-k A schematic diagram of the target image obtained by the algorithm; Figure 3(b) shows a schematic diagram of obtaining the target image using the L1 thresholding algorithm based on two-step iteration; Figure 3(c) shows a schematic diagram of the target image obtained by the improved sparse reconstruction algorithm according to an embodiment of this application; Figure 4 The diagram shows a cross-sectional view of the target image obtained by the method shown in Figure 3(a), the method shown in Figure 3(b), and the method shown in Figure 3(c) of this application in the azimuth direction. Figure 5 This diagram illustrates a quantitative analysis of the peak side lobe ratio (PSLR), integral side lobe ratio (ISLR), and impulse response width (IRW) of a target point according to an embodiment of this application. Figure 6(a) illustrates a wavenumber domain imaging algorithm based on conventional techniques with 30% random missing data in the azimuth direction. ω-k A schematic diagram of the imaging results (from the algorithm); Figure 6(b) shows a schematic diagram of the imaging results based on the two-step iterative L1 thresholding algorithm with 30% random missing data in the azimuth direction; Figure 6(c) shows a schematic diagram of the imaging results of an embodiment of this application with 30% random missing data in the azimuth direction; Figure 7 This diagram illustrates a quantitative analysis of PSLR, LR, and IRW of a point target using echo data with 30% random missing data in the azimuth direction, according to an embodiment of this application. Figure 8 The following is an illustration based on L according to an embodiment of this application. 1 / 2 A schematic block diagram of a regularized synthetic aperture radar imaging system. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions of the embodiments of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Synthetic Aperture Radar (SAR), a type of microwave remote sensing imaging radar, boasts high azimuth resolution and all-weather, all-day, and long-range observation capabilities, leading to its widespread application. However, radar is susceptible to interference from various factors during actual operation, resulting in missing sampling data, decreased signal-to-noise ratio, and consequently, imaging quality that fails to meet practical requirements. Generally, signal sampling must satisfy the Nyquist sampling theorem. As the amount of information contained in SAR echo data gradually increases, the amount of echo data obtained through traditional sampling methods also gradually increases, placing higher demands on the storage and computation of echo data.

[0019] Compressed sensing (CS) theory shows that when the signal is sparse, sparse signal reconstruction can be achieved with fewer samples. Specifically, the process of acquiring the scene scattering coefficients in a compressed sensing SAR imaging system can be represented by a linear time-invariant system: ; in, y Represents the echo data vector, and y ∈ C M×1 ; x Represents the scene scattering coefficient, and x ∈ C N×1 ;n Let represent a Gaussian white noise vector, and n ∈ C M×1 Θ= HΨ ∈ C M×N Represents the observation matrix. H ∈ C Q×M This represents the sparse microwave imaging undersampling matrix. Q This indicates the number of sampling points in sparse microwave imaging echo data. P ∈ C M×N Let represent the measurement matrix. The above formula can be solved using L1 norm regularization: ; in, l >0 indicates the regularization parameter. x Represents the scene scattering coefficient. y Θ represents the echo data vector, and Θ represents the observation matrix.

[0020] This method, based on the fact that convex relaxation of L1 regularization cannot produce sufficiently sparse solutions, introduces L... 1 / 2 Regularization not only enables sparse signal reconstruction with fewer samples but also allows for rapid solution. However, sparse reconstruction algorithms for SAR image reconstruction require transforming echo data from a two-dimensional matrix into a one-dimensional vector. When processing a large number of echo data samples or a large spatial scale of the imaging scene, the data consumes significant memory and processing time is long. Therefore, this imaging method suffers from high computational complexity and poor real-time performance, making it unsuitable for real-time imaging of large scenes. To address these issues, researchers have proposed a sparse imaging method based on approximate observation. This method uses approximate observation operators instead of matrix multiplication to construct a new sparse self-focusing model, significantly reducing the dimensionality of the observation matrix and improving reconstruction efficiency and imaging quality. This application introduces the Accelerated Proximal Gradient (APG) algorithm to solve L 1 / 2 The regularization problem combines the approximate observation operator and the fast solution of L. 1 / 2 By combining new algorithms for regularization problems, a novel sparse reconstruction model is constructed. The observation matrix is ​​replaced by an approximate observation operator, which reduces memory usage, speeds up reconstruction, and improves imaging quality.

[0021] Based on at least one of the above problems, this application proposes a method based on L 1 / 2A regularized synthetic aperture radar (SAR) imaging method, comprising: acquiring echo data from sampling points; decomposing an imaging operator based on a wavenumber domain imaging algorithm into a phase correction operator and an azimuth correction operator; and using the phase correction operator and the azimuth correction operator, employing a fast solution for L... 1 / 2 A regularized threshold iteration algorithm is used to process the echo data to obtain a first intermediate quantity; the first intermediate quantity is then used as an operator for iterative calculation until a preset condition is met, resulting in a synthetic aperture radar imaging model. This embodiment of the application is based on L... 1 / 2 The regularized synthetic aperture radar (SAR) imaging method decomposes the imaging operator based on the wavenumber domain imaging algorithm into a phase correction operator and an azimuth correction operator. A first intermediate quantity is obtained through the phase correction operator and the azimuth correction operator. Then, the first intermediate quantity is used as an operator for iterative calculation to obtain the SAR imaging model. The embodiments of this application are based on a sparse imaging method of approximate observation. Approximate observation operators are used to replace matrix multiplication to construct a new sparse self-focusing model, which greatly reduces the dimension of the observation matrix and improves reconstruction efficiency and imaging quality.

[0022] Figure 1 The following is an illustration based on L according to an embodiment of this application. 1 / 2 A schematic flowchart of a regularized synthetic aperture radar imaging method; such as Figure 1 As shown, according to the embodiments of this application, based on L... 1 / 2 The regularized synthetic aperture radar imaging method 100 may include the following steps S101, S102, S103 and S104: In step S101, the echo data of the sampling point is acquired.

[0023] In one embodiment of this application, the method further includes: Initialization processing is performed based on the aforementioned wavenumber domain imaging algorithm; The initialization process includes setting parameters based on the wavenumber domain imaging algorithm.

[0024] For example, the following parameters can be set during initialization: i =0, maximum number of iterations I max , R 0=0, t 0=1, and the phase error matrix F PGA ( G ( L ⊙ S ));in, i Indicates the number of iterations. R 0 represents the initial value of the auxiliary variable. t0 represents the initial value of the time step.

[0025] In step S102, the imaging operator based on the wavenumber domain imaging algorithm is decomposed into a phase correction operator and an azimuth correction operator.

[0026] In one embodiment of this application, the azimuth correction operator is obtained by decomposing the imaging operator based on the wavenumber domain imaging algorithm, including: performing an inverse Fourier transform on the two-dimensional echo data to obtain the azimuth correction operator based on the imaging operator based on the wavenumber domain imaging algorithm.

[0027] The imaging operator is represented as follows: ; in, Y This represents two-dimensional synthetic aperture radar echo data. F ɑ This indicates a Fourier transform of the orientation. F r This represents a distance-to-Fourier transform. F a-1 This indicates the inverse Fourier transform of the orientation; F r-1 This represents the inverse Fourier transform of distance; i ref This represents the uniform distance migration correction matrix. S (·) denotes the Stolt interpolation operator.

[0028] In one example, the imaging operator I ω-k (·) is decomposed into a phase correction operator Oh (·) and orientation-to-Fourier transform operator F ɑ (·),Right now , ;in, Y Represents a random variable; F ɑ and F r These represent the azimuth-to-Fourier transform and the range-to-Fourier transform, respectively. i ref This represents the uniform distance migration correction matrix. Substituting the above formula into... The following formula can be obtained from this, where Θ is the observation matrix.

[0029] ; in, x Represents the scene scattering coefficient; F Represents the phase error matrix; y Represents the echo data vector; L The undersampling matrix represents the echo;F a This represents the azimuth-to-Fourier transform operator; l Represents the regularization parameter; Oh This represents the phase correction operator.

[0030] The purpose of decomposing the imaging operator here is to separate the amplitude (distance) and phase of the echo data so that amplitude and phase corrections can be performed subsequently.

[0031] In step S103, the phase correction operator and the azimuth correction operator are used to quickly solve for L. 1 / 2 A regularized threshold iteration algorithm is used to process the echo data to obtain a first intermediate value.

[0032] In one embodiment of this application, the first intermediate quantity is represented as follows: ; in, R i+1 Indicates the first i The auxiliary variable for the +1 iteration is used to update the... i +1 scene scattering coefficient; H λ，μ，1 / 2 Indicates the threshold operator; R i Indicates the first i The auxiliary variable for the second iteration is used to update the third iteration. i Scattering coefficients for each scene; L The undersampling matrix represents the echo; y Represents the echo data vector; F a This represents the azimuth-to-Fourier transform operator; Oh This represents the phase correction operator; F Represents the phase error matrix; d This indicates the iteration step size.

[0033] in, H λ，μ，1 / 2 It can be represented as follows: ; in, z Represents variables; or λ,μ,1 / 2 Let C denote the threshold function, T denote the transpose of the matrix; N Represents the set of complex numbers.

[0034] Wherein, threshold function or λ，μ，1 / 2 The expression is as follows:

[0035] in,f λ,μ,1 / 2 The expression is as follows: , in, l This represents the regularization parameter, and ; m This represents a parameter that controls the convergence speed of the gradient descent algorithm.

[0036] In step S104, the first intermediate quantity is used as an operator for iterative calculation until the preset conditions are met, and a synthetic aperture radar imaging model is obtained.

[0037] In one embodiment of this application, the target phase of the aperture radar imaging model is obtained by iteratively calculating the first intermediate quantity using the phase correction operator.

[0038] In one embodiment of this application, the first intermediate quantity is used as an operator for iterative calculation, wherein the calculation formula is as follows: ; in, x Represents the scene scattering coefficient; R i+1 Indicates the first i The auxiliary variable for the +1 iteration is used to update the... i +1 scene scattering coefficient; R i Indicates the first i The auxiliary variable for the second iteration is used to update the third iteration. i Scattering coefficients for each scene; t i Indicates the first i The time step of each iteration; t i+1 Indicates the first i +1 iteration time step.

[0039] in t i+1 The expression is as follows: ; in, t Indicates the time step.

[0040] In this embodiment of the application, the result can be calculated based on the first intermediate quantity. x i+1 .

[0041] In one embodiment of this application, the solution for the first part can be obtained using the following formula. m Error phase of each azimuth sampling point: ; in, Oh This represents the phase correction operator; y Represents the echo data vector; F ɑ This represents the azimuth-to-Fourier transform operator; X ] m Representation matrix X The m Row; H denotes the conjugate transpose of the matrix; x i+1 Indicates the iteration number i +1 scene scattering coefficient.

[0042] The phase error matrix is ​​as follows:

[0043] in, f Indicates the error phase; M Indicates the number of upsampling points from the distance; j Represents an imaginary number.

[0044] In this embodiment of the application, phase correction can be performed on each sampling point in the intermediate data according to the error phase.

[0045] In one example, during iterative computation, one can... R i+1 Assign to R i , F i+1 Assign to F i Then proceed to the next iteration, and stop iterating when the preset conditions are met.

[0046] In one embodiment of this application, the preset conditions include: ; in, x Represents the scene scattering coefficient; e This indicates the preset threshold value.

[0047] The embodiments of this application are based on L 1 / 2 The regularized synthetic aperture radar (SAR) imaging method decomposes the imaging operator based on the wavenumber domain imaging algorithm into a phase correction operator and an azimuth correction operator. A first intermediate quantity is obtained through the phase correction operator and the azimuth correction operator. Then, the first intermediate quantity is used as an operator for iterative calculation to obtain the SAR imaging model. The embodiments of this application are based on a sparse imaging method of approximate observation. Approximate observation operators are used to replace matrix multiplication to construct a new sparse self-focusing model, which greatly reduces the dimension of the observation matrix and improves reconstruction efficiency and imaging quality.

[0048] This application also provides comparative examples of target images obtained using conventional techniques and the technical solutions of this application.

[0049] In the first example, the SAR imaging simulation parameters set in the initialization step are as follows: Figure 2 As shown. Based on the above parameters, the wavenumber domain imaging algorithm (from traditional techniques) is then used. ω-k The target image obtained by the algorithm is shown in Figure 3(a). Figure 3(a) clearly shows that the point target has abundant sidelobes. Further, an L1 thresholding algorithm based on two-step iterations can be used to obtain the target image shown in Figure 3(b). Compared to Figure 3(a), the sidelobes of the point target are significantly reduced, and the imaging quality is improved. The target image obtained using the improved sparse reconstruction algorithm provided in this application is shown in Figure 3(c). Figure 3(c) shows that the energy of the target point is significantly increased, and the imaging quality is significantly improved.

[0050] In the second example, such as Figure 4 The image shown is a cross-sectional view in the azimuth direction of the target image obtained using the methods shown in Figure 3(a), Figure 3(b), and Figure 3(c) of this application. Figure 4 It is evident that wavenumber domain imaging algorithms in traditional techniques ( ω-k The algorithm has the worst resolution in the azimuth direction. Compared with traditional wavenumber domain imaging algorithms, the L1 thresholding algorithm based on two-step iteration has a significant improvement in azimuth resolution; while the sparse reconstruction algorithm constructed in this application has better azimuth resolution than the L1 thresholding algorithm based on two-step iteration.

[0051] like Figure 5 The diagram illustrates the quantitative analysis of the Peak Side Lobe Ratio (PSLR), Integral Side Lobe Ratio (ISLR), and Impulse Response Width (IRW) of a target point. Compared to the methods shown in Figure 3(a) and Figure 3(b), the sparse reconstruction algorithm involved in this application has higher azimuth resolution, and the method of this application has a lower PSLR, so the main lobe of a weak target is less likely to be overwhelmed by the sidelobes of neighboring strong targets.

[0052] In the third example, as shown in Figure 6(a), a wavenumber domain imaging algorithm in a conventional technique is presented when 30% of the data is randomly missing in the azimuth direction. ω-kThe imaging results of the algorithm are shown in Figure 6(b). Figure 6(c) shows the imaging results of the L1 thresholding algorithm based on two-step iteration with 30% random missing data in the azimuth direction. As can be seen from Figures 6(a) to 6(c), compared with the wavenumber domain imaging algorithm in traditional technology ( ω-k Compared with the imaging algorithm and the L1 thresholding algorithm based on two-step iteration under the condition of 30% random missing data in the azimuth direction, the imaging method of this application produces clearer imaging results, higher image quality, and significantly improved focusing performance.

[0053] like Figure 7 The image shows a quantitative analysis of PSLR, LR, and IRW for point targets using echo data with 30% random missing data in the azimuth direction. Figure 7 The data clearly shows that the imaging method provided in this application is superior to the other two algorithms in all aspects, thus proving the advantage of the sparse reconstruction algorithm constructed in this application in improving image quality.

[0054] In this embodiment of the application, ω-k The algorithm, as an approximate observation operator, is used for fast solving of L. 1 / 2 By combining regularization algorithms, a new sparse reconstruction model is constructed, thereby reducing the computational load of SAR imaging and improving the imaging effect of the target.

[0055] The following is combined Figure 8 Based on L in this application 1 / 2 A regularized synthetic aperture radar imaging system is described, in which... Figure 8 The following is an illustration based on L according to an embodiment of this application. 1 / 2 A schematic block diagram of a regularized synthetic aperture radar imaging system.

[0056] like Figure 8 As shown, based on L 1 / 2 A regularized synthetic aperture radar (SAR) imaging system 800 includes one or more memories 801 and one or more processors 802. The memories 801 store a computer program executed by the processors 802. When executed by the processors 802, the computer program causes the processors 802 to perform the L-based... 1 / 2 A regularized synthetic aperture radar imaging method.

[0057] Based on L 1 / 2 A regularized synthetic aperture radar imaging system 800 can be implemented using software, hardware, or a combination of both, based on L... 1 / 2 Part or all of the computer equipment for regularized synthetic aperture radar imaging methods.

[0058] like Figure 8 As shown, based on L 1 / 2 The regularized synthetic aperture radar imaging system 800 includes one or more memories 801, one or more processors 802, a display (not shown), and a communication interface, etc., which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). It should be noted that... Figure 8 The L-based 1 / 2 The components and structure of the regularized synthetic aperture radar imaging system 800 are merely exemplary and not limiting; based on L... 1 / 2 The regularized synthetic aperture radar imaging system 800 can also have other components and structures.

[0059] Memory 801 is used to store various data and executable program instructions generated during the operation of the method, such as algorithms for storing various application programs or implementing various specific functions. It may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0060] The processor 802 may be a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other processing units with data processing and / or instruction execution capabilities, and may be based on L... 1 / 2 Other components in the regularized synthetic aperture radar imaging system 800 are configured to perform the desired functions.

[0061] In one example, based on L 1 / 2 The regularized synthetic aperture radar imaging system 800 also includes an output device that can output various information (such as images or sounds) to the outside (e.g., a user), and may include one or more of a display device, a speaker, etc.

[0062] The communication interface can be any known communication protocol interface, such as a wired interface or a wireless interface. The communication interface may include one or more serial ports, USB interfaces, Ethernet ports, WiFi, wired networks, DVI interfaces, device integrated interconnect modules, or other suitable ports, interfaces, or connections.

[0063] Furthermore, according to embodiments of this application, a storage medium is also provided, on which program instructions are stored, which, when executed by a computer or processor, are used to perform the L-based embodiments of this application. 1 / 2 The corresponding steps of a regularized synthetic aperture radar imaging method. The storage medium may include, for example, a memory card for a smartphone, a storage component for a tablet computer, a hard disk for a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media.

[0064] The embodiments of this application are based on L 1 / 2 Regularized synthetic aperture radar imaging systems and storage media, due to their ability to achieve the aforementioned L-based... 1 / 2 Regularized synthetic aperture radar imaging methods therefore possess the same advantages as the aforementioned L-based methods. 1 / 2 It has the same advantages as regularized synthetic aperture radar imaging methods.

[0065] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0068] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0069] Similarly, it should be understood that, in order to streamline this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, this approach should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0070] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or elements of any method or apparatus so disclosed may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0071] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0072] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0073] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0074] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method based on L 1 / 2 A regularized synthetic aperture radar imaging method, characterized in that, The method includes: Acquire echo data from the sampling points; The imaging operator based on the wavenumber domain imaging algorithm is decomposed into a phase correction operator. Ω (·) and azimuth correction operator F ɑ (·), , ;in, Y This represents the raw echo data from a two-dimensional SAR system. F ɑ and F r These represent the azimuth-to-Fourier transform and the range-to-Fourier transform, respectively. F a -1 and F r -1 These represent the inverse Fourier transform of azimuth and the inverse Fourier transform of range, respectively. θ ref This represents the uniform distance migration correction matrix. S (·) denotes the Stolt interpolation operator; Using the phase correction operator and the azimuth correction operator, a fast solution for L is obtained. 1 / 2 A regularized threshold iteration algorithm is used to process the echo data to obtain a first intermediate quantity; The first intermediate quantity is used as an operator for iterative calculation until the preset conditions are met, and a synthetic aperture radar imaging model is obtained. The first intermediate quantity is represented as follows: ； in, R i+1 Indicates the first i The auxiliary variable for the +1 iteration is used to update the... i +1 scene scattering coefficient; H λ，μ，1 / 2 Indicates the threshold operator; R i Indicates the first i The auxiliary variable for the second iteration is used to update the third iteration. i Scattering coefficients for each scene; L The undersampling matrix represents the echo data; y Represents the echo data vector; F a This represents the azimuth-to-Fourier transform operator; Ω This represents the phase correction operator; Φ Represents the phase error matrix; δ Indicates the iteration step size; The imaging operator decomposition based on the wavenumber domain imaging algorithm yields the azimuth correction operator, including: the imaging operator based on the wavenumber domain imaging algorithm, and the azimuth correction operator obtained by performing inverse Fourier transform on the two-dimensional echo data. The imaging operator is represented as follows: 。 2. The method according to claim 1, characterized in that, The first intermediate quantity is used as an operator for iterative calculation, and the calculation formula is as follows: ； in, x Represents the scene scattering coefficient; R i+1 Indicates the first i The auxiliary variable for the +1 iteration is used to update the... i +1 scene scattering coefficient; R i Indicates the first i The auxiliary variable for the second iteration is used to update the third iteration. i Scattering coefficients for each scene; t i Indicates the first i The time step of each iteration; t i+1 Indicates the first i +1 iteration time step.

3. The method according to claim 1, characterized in that, The method further includes: iteratively calculating the first intermediate quantity using the phase correction operator to obtain the target phase of the aperture radar imaging model.

4. The method according to claim 1, characterized in that, The method further includes: Initialization processing is performed based on the aforementioned wavenumber domain imaging algorithm; The initialization process includes setting parameters based on the wavenumber domain imaging algorithm.

5. The method according to claim 1, characterized in that, The preset conditions include: ； in, x Represents the scene scattering coefficient; ε This indicates the preset threshold value.

6. A method based on L 1 / 2 A regularized synthetic aperture radar imaging system, characterized in that, The system includes: A memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program, when executed by the processor, causes the processor to perform the L-based operation as described in any one of claims 1 to 5. 1 / 2 A regularized synthetic aperture radar imaging method.

7. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the L-based method as described in any one of claims 1 to 5. 1 / 2 A regularized synthetic aperture radar imaging method.

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