Self-focusing motion compensation method based on sample concatenation screening and local contrast weighting

Abstract

This application belongs to the field of SAR imaging technology. It provides a self-focusing motion compensation method based on sample cascaded screening and local contrast weighting. The technology disclosed in this application is used to correct motion errors in two-dimensional matched-filtered image data during synthetic aperture radar imaging, estimating and compensating for phase errors caused by non-ideal motion during platform flight. This method improves the adaptability of traditional self-focusing motion compensation algorithms in complex scenarios in practical applications. It can adaptively perform sample screening and windowing filtering, constructing better weighting coefficients to improve phase estimation accuracy, enhance SAR imaging performance, and provide support for subsequent target detection, recognition, and interpretation.

Description

Technical Field

[0001] This disclosure relates to the field of SAR imaging technology, and in particular to a self-focusing motion compensation method based on sample cascade screening and local contrast weighting. Background Technology

[0002] SAR imaging technology achieves high-resolution azimuth by creating a virtual large aperture through platform motion, thereby obtaining high-resolution remote sensing images of the observed scene. Platform motion is both fundamental to imaging and one of the main factors affecting image quality. In airborne SAR imaging, atmospheric disturbances and other factors can cause positional shift errors in the antenna phase center. Motion errors lead to defocusing and geometric distortion in the image, making motion compensation essential for airborne high-resolution imaging. In practice, motion error compensation is typically based on data autofocus techniques. Autofocus utilizes the inherent clustering characteristics of images to adaptively extract the error phase from certain high-quality data samples. A typical example is the highlighting point initial phase correction method, which uses multiple isolated scattering units as data samples to estimate the initial phase error. Autofocus techniques are particularly important for imaging mounted on small platforms. Small platforms require miniaturization, low weight, and low cost, which necessitates the use of small, lightweight, low-precision inertial navigation systems. Furthermore, small platforms are more sensitive to atmospheric disturbances; significant platform turbulence not only introduces large phase errors but also causes envelope shifts. Furthermore, in order to obtain wide-scene imaging under low-altitude conditions, the radar must operate at a low elevation angle, which inevitably leads to strong spatial variability in motion errors, which needs to be overcome in adaptive motion error compensation.

[0003] Existing phase gradient autofocus algorithms do not adhere strictly to the phase form in their phase estimation and, in principle, do not distinguish between higher-order and lower-order terms, enabling phase compensation over a wider range. They overcome the dependence of traditional global algorithms on "signal statistical stability," accurately capturing local phase distortions caused by high-frequency errors, exhibiting excellent robustness and high precision. They demonstrate good performance in phase error estimation for single-point scenes and are suitable for imaging processing in small scenes with minimal motion errors. However, in large scenes, when errors are significant and envelope shifts occur, or when the scene is complex or the signal-to-noise ratio is low, their effectiveness is significantly reduced. Existing PGA methods rely too heavily on the characteristics of single prominent points in the scene and use the maximum energy criterion for sample selection. This can easily lead to the misselection of low-noise units and units with multiple prominent points, reducing the accuracy of phase estimation and deteriorating motion compensation. Using a local contrast selection criterion can identify distance units that are closer to single prominent points, reducing the misselection rate and improving the algorithm's adaptability. Furthermore, fixed window length filtering requires prior scene information, resulting in insufficient adaptability. Additionally, previous weighted PGA methods often used signal-to-noise ratio (SNR) weighting to increase the weights of prominent point units, using weight coefficients to characterize the contribution of distance units to phase estimation. However, in practical applications, multiple targets may exist within a single distance gate, making it impossible to determine the SNR within the current gate. Moreover, units with multiple prominent points have higher SNR and larger weights, which can skew phase estimation and affect image focusing quality. Therefore, it is necessary to adjust the weighting coefficient construction criteria to distinguish the weights of single prominent points, multiple prominent points, and low-noise units, thereby achieving accurate phase estimation.

[0004] Therefore, it is necessary to improve one or more of the problems existing in the above-mentioned related technical solutions.

[0005] It should be noted that this section is intended to provide background or context for the technical solutions of this disclosure as set forth in the claims. The description herein does not constitute an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0006] The purpose of this disclosure is to provide a self-focusing motion compensation method based on sample cascade screening and local contrast weighting, thereby overcoming at least to some extent one or more problems caused by the limitations and defects of related technologies.

[0007] According to embodiments of this disclosure, a self-focusing motion compensation method based on sample cascade screening and local contrast weighting is provided, comprising: Step S1: Perform envelope error fine correction on the echo data after distance travel correction to obtain the corrected image data; Step S2: Perform range cell cascade filtering on the calibrated data to select high-quality range cells; Step S3: Reorganize the selected high-quality distance cells into a new matrix and perform azimuth pulse compression to move the selected prominent point region to the azimuth center of the matrix. Step S4: Preset multiple different window lengths, use each window length to perform window filtering on the data matrix processed in step S3, estimate the phase, compensate the phase, calculate the contrast of the focused image corresponding to each window length, and select the window length with the highest contrast as the optimal window length for image filtering. Step S5: Construct weighted coefficients for each distance unit based on the contrast criterion, and perform weighted phase gradient estimation on the filtered image data from Step S4; Step S6: Integrate the estimated phase gradient to obtain the phase error to be compensated, and repeat steps S3 to S5 until the iteration converges to complete the error phase compensation and obtain the compensated SAR image.

[0008] Further, in step S1, the envelope error fine correction includes: Construct the target signal after distance movement correction; A coarse estimation of the phase gradient is performed by downsampling, the phase estimate is obtained by integration, and the remaining distance migration is calculated based on the form of the target signal. An envelope fine alignment compensation function is constructed based on the remaining distance migration, and the envelope is finely aligned to obtain the corrected data.

[0009] Furthermore, in step S2, the cascade filtering specifically includes: Calculate the energy of each distance cell and select the high-energy distance cells; High-quality range cells are selected from high-energy range cells based on local contrast. Calculate the local contrast of the selected distance cells, set a contrast threshold, and filter out distance cells with local contrast exceeding the threshold to select high-quality distance cells.

[0010] 4. The self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to claim 3, characterized in that, in step S4, the selection process of the optimal window length is as follows: For multiple preset window lengths, phase estimation and compensation are performed on the filtered data, the contrast of the compensated image is calculated, and the window length corresponding to the maximum contrast value is taken as the optimal window length for the current highlight area.

[0011] Furthermore, in step S5, the formula for weighted phase gradient estimation is:

[0012] in, For angle calculation symbols, For the conjugate of a vector or matrix, The target signal after discretization. To and The conjugate of discrete signals of adjacent azimuth units. Indicates the first The weights of each distance cell.

[0013] Furthermore, the first The formula for the weight of each distance cell is:

[0014] in, This represents the standard deviation of the current distance from the door. The amplitude of a pixel. This represents the average amplitude of the current distance from the gate.

[0015] Furthermore, in step S6, the condition for iterative convergence is: The phase error change estimated between two consecutive iterations is less than a preset threshold, or the preset maximum number of iterations is reached.

[0016] According to a second aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the self-focusing motion compensation method based on sample cascade screening and local contrast weighting as described in any of the above embodiments.

[0017] According to a third aspect of the present disclosure, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to execute the steps of the self-focusing motion compensation method based on sample cascade screening and local contrast weighting in any of the above embodiments by executing the executable instructions.

[0018] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: In the embodiments of this disclosure, the self-focusing motion compensation method based on sample cascade screening and local contrast weighting, as described above, performs further refined correction on the envelope error after range walk correction. This step aims to effectively constrain the energy distribution corresponding to the same target, preventing it from spreading between multiple adjacent range gates, thereby improving the signal concentration in the range direction and providing a good foundation for accurately selecting the appropriate range gate in subsequent processing. Based on this, by designing a cascade screening mechanism of "energy + contrast," the system can select range units with higher signal-to-noise ratios and more stable features from candidate units, providing higher-quality input for subsequent phase estimation. On the other hand, a dual-criteria strategy is used to construct weighting coefficients to achieve refined quantification of the contribution of different units. Specifically, single-characteristic points, multiple-characteristic points, and noisy background units are evaluated separately. By reasonably setting weights, the dominant role of single-characteristic points in phase estimation is enhanced, while suppressing the biasing effect that multiple-characteristic points may cause due to phase fluctuations. This mechanism significantly improves the accuracy and robustness of phase estimation, thereby achieving high-precision compensation for error phases. Ultimately, the above processing can effectively improve the focusing quality of SAR images, giving them clearer targets and better visual effects.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0021] Figure 1 A flowchart illustrating the steps of a self-focusing motion compensation method based on sample cascade screening and local contrast weighting in an exemplary embodiment of this disclosure is shown. Figure 2 A flowchart illustrating the implementation of the self-focusing motion compensation method based on sample cascade screening and local contrast weighting in an exemplary embodiment of this disclosure is shown. Figure 3 This illustration shows an image after focusing measured data using a conventional PGA in an exemplary embodiment of this disclosure; Figure 4 This illustration shows an image of the measured data after focusing using the method of this application in an exemplary embodiment of this disclosure; Figure 5 This illustration shows a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure; Figure 6 This diagram illustrates an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0023] Furthermore, the accompanying drawings are merely illustrative diagrams of embodiments of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0024] This example implementation first provides a self-focusing motion compensation method based on sample cascade screening and local contrast weighting. (See reference...) Figure 1 As shown, the method may include the following steps: Step S1: Perform envelope error fine correction on the echo data after distance travel correction to obtain the corrected image data; Step S2: Perform range cell cascade filtering on the calibrated data to select high-quality range cells; Step S3: Reorganize the selected high-quality distance cells into a new matrix and perform azimuth pulse compression to move the selected prominent point region to the azimuth center of the matrix. Step S4: Preset multiple different window lengths, use each window length to perform window filtering on the data matrix processed in step S3, estimate the phase, compensate the phase, calculate the contrast of the focused image corresponding to each window length, and select the window length with the highest contrast as the optimal window length for image filtering. Step S5: Construct weighted coefficients for each distance unit based on the contrast criterion, and perform weighted phase gradient estimation on the filtered image data from Step S4; Step S6: Integrate the estimated phase gradient to obtain the phase error to be compensated, and repeat steps S3 to S5 until the iteration converges to complete the error phase compensation and obtain the compensated SAR image.

[0025] The self-focusing motion compensation method based on cascaded sample selection and local contrast weighting, as described above, performs further refined correction on the envelope error after range travel correction. This step aims to effectively constrain the energy distribution corresponding to the same target, preventing it from spreading between multiple adjacent range gates, thereby improving the signal concentration in the range direction and providing a good foundation for accurately selecting the appropriate range gate in subsequent processing. Based on this, by designing a cascaded selection mechanism of "energy + contrast," the system can select range units with higher signal-to-noise ratios and more stable features from candidate units, providing higher-quality input for subsequent phase estimation. On the other hand, a dual-criteria strategy is used to construct weighting coefficients to achieve refined quantification of the contribution of different units. Specifically, single-characteristic points, multi-characteristic points, and noisy background units are evaluated separately. By reasonably setting weights, the dominant role of single-characteristic points in phase estimation is enhanced, while suppressing the biasing effect that multi-characteristic points may cause due to phase fluctuations. This mechanism significantly improves the accuracy and robustness of phase estimation, thereby achieving high-precision compensation for error phases. Ultimately, the above processing can effectively improve the focusing quality of SAR images, giving them clearer targets and better visual effects.

[0026] Below, we will refer to Figures 1 to 4 The steps of the method described above in this example embodiment will be explained in more detail.

[0027] In one embodiment, such as Figure 2 The diagram shown is a flowchart illustrating the implementation of a self-focusing motion compensation method based on sample cascade screening and local contrast weighting.

[0028] In step S1, the envelope error is finely corrected on the echo data after distance travel correction to obtain the corrected image data.

[0029] Specifically, the envelope error after distance movement correction is finely corrected.

[0030] 1a) Assume the target signal after range pulse compression and range travel correction is:

[0031] in, , , and These represent the target scattering coefficient, the instantaneous slant range after range travel correction, the slant range due to motion error, the exponential operation, and the azimuth and range envelopes, respectively. The phase of motion error compensation is the last term.

[0032] 1b) Set the initial downsampling rate to 1 / 3. By gradually reducing the downsampling rate and using an iterative approach, a relatively accurate phase gradient is obtained. The phase estimate is then obtained through integration. Based on the form of the target signal, the distance to be compensated can be obtained:

[0033] 1c) Based on the estimated remaining distance migration The envelope error fine alignment compensation function is obtained as follows:

[0034] In step S2, after the envelope error is finely processed, the target energy in the scene is focused on the corresponding range cell, and the range cell is subjected to cascaded sample screening.

[0035] 2a) Calculate the energy of the m-th distance unit. ; 2b) Set threshold Select several distance gates with relatively high energy; 2c) Calculate the local contrast of the selected distance gates.

[0036] in, This represents the local contrast calculation area selected by the current distance gate with the m-th orientation cell as the center. Indicates the amplitude of a pixel. This indicates the total number of orientation units at the current distance from the gate; a threshold is set. Select several distance gates with high local contrast in a larger energy area, remove those with high noise levels, and filter out distance units with multiple display points. In step S3, the distance cells selected in the second screening are reorganized into a new matrix, and azimuth pulse compression is performed to move the selected prominent point region to the azimuth center position of the matrix. In step S4, multiple sets of different window lengths are preset. By using different window lengths to apply window filtering to the scene, the signal-to-noise ratio of the corresponding distance unit is improved. The phase is estimated by using multiple sets of window lengths, the phase-compensated data is focused, the contrast of the focused area is calculated, and the window length with the highest contrast is selected as the optimal window length for the current position. This window length is then used for image filtering to avoid the problem of insufficient window length in dense target areas or redundant window length in open areas due to fixed window lengths.

[0037] In step S5, a weighted phase gradient estimation is performed on the distance cell based on the contrast criterion.

[0038] in, For angle calculation symbols, Represents the conjugate of a vector or matrix. Indicates the first The weights of each distance cell are calculated using the following formula:

[0039] in, This represents the standard deviation of the current distance gate, which further increases the weight of the single prominent point distance unit, better distinguishes the contribution of different types of distance units to phase estimation, and reduces the dependence on samples.

[0040] In step S6, the estimated phase gradient is integrated to obtain the phase to be compensated. Steps S3 to S5 are repeated until the iteration converges, and the error phase compensation is completed.

[0041] In a specific embodiment, the image focusing result after motion compensation using the traditional PGA method is as follows: Figure 3 As shown, angular targets with larger distance intervals can be roughly distinguished, but there is side lobe broadening in the azimuth direction, while angular targets with smaller distance intervals cannot be completely distinguished; after using the motion compensation method proposed in this application, the image focusing result is as follows. Figure 4 As shown, angular targets with small distance intervals can be clearly distinguished, and azimuth spread is well suppressed.

[0042] In summary, this application is used for motion error correction of two-dimensional matched-filtered image data in synthetic aperture radar (SAR) imaging, and for estimating and compensating for phase errors caused by non-ideal motion during platform flight. It employs a cascaded sample selection process using energy optimization and optimal local contrast criteria. The modified sample selection criteria highlight local detail contrast, resulting in more accurate atom selection. Subsequent adaptive windowing filtering enhances the algorithm's adaptability to complex environments. By constructing weighting coefficients using image contrast and adaptively scaling the contribution of single-highlighted points, multiple-highlighted points, and noise floor units, the phase estimation accuracy is further improved, making it suitable for motion compensation during non-stationary platform flight in complex environments.

[0043] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps. Furthermore, it is readily understood that these steps may be executed synchronously or asynchronously, for example, in multiple modules / processes / threads.

[0044] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, having stored thereon a computer program that, when executed by a processor, can implement the steps of the self-focusing motion compensation method based on sample cascade screening and local contrast weighting as described in any of the above embodiments. In some possible implementations, various aspects of this application can also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this application described in the section on the self-focusing motion compensation method based on sample cascade screening and local contrast weighting described in this specification.

[0045] refer to Figure 5 As shown, a program product 300 for implementing the above-described method according to an embodiment of this application is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product of this application is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0046] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0047] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0048] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0049] In exemplary embodiments of this disclosure, an electronic device is also provided, which may include a processor and a memory for storing executable instructions of the processor. The processor is configured to perform the steps of the self-focusing motion compensation method based on sample cascade screening and local contrast weighting as described in any of the above embodiments by executing the executable instructions.

[0050] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0051] The following reference Figure 6 To describe an electronic device 600 according to this embodiment of the present application. Figure 6 The electronic device 600 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0052] like Figure 6 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0053] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the section on the self-focusing motion compensation method based on sample cascade screening and local contrast weighting of this specification, according to various exemplary embodiments of this application. For example, the processing unit 610 can perform, as follows: Figure 1 The steps are shown in the figure.

[0054] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only memory unit (ROM) 6203.

[0055] The storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0056] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0057] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0058] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the above-described self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to the embodiments of this disclosure.

[0059] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A self-focusing motion compensation method based on sample cascade screening and local contrast weighting, characterized in that, include: Step S1: Perform envelope error fine correction on the echo data after distance travel correction to obtain the corrected image data; Step S2: Perform range cell cascade filtering on the calibrated data to select high-quality range cells; Step S3: Reorganize the selected high-quality distance cells into a new matrix and perform azimuth pulse compression to move the selected prominent point region to the azimuth center of the matrix. Step S4: Preset multiple different window lengths, use each window length to perform window filtering on the data matrix processed in step S3, estimate the phase, compensate the phase, calculate the contrast of the focused image corresponding to each window length, and select the window length with the highest contrast as the optimal window length for image filtering. Step S5: Construct weighted coefficients for each distance unit based on the contrast criterion, and perform weighted phase gradient estimation on the filtered image data from Step S4; Step S6: Integrate the estimated phase gradient to obtain the phase error to be compensated, and repeat steps S3 to S5 until the iteration converges to complete the error phase compensation and obtain the compensated SAR image.

2. The self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to claim 1, characterized in that, In step S1, the envelope error fine correction includes: Construct the target signal after distance movement correction; A coarse estimation of the phase gradient is performed by downsampling, the phase estimate is obtained by integration, and the remaining distance migration is calculated based on the form of the target signal. An envelope fine alignment compensation function is constructed based on the remaining distance migration, and the envelope is finely aligned to obtain the corrected data.

3. The self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to claim 2, characterized in that, In step S2, the cascading filtering specifically includes: Calculate the energy of each distance cell and select the high-energy distance cells; High-quality range cells are selected from high-energy range cells based on local contrast. Calculate the local contrast of the selected distance cells, set a contrast threshold, and filter out distance cells with local contrast exceeding the threshold to select high-quality distance cells.

4. The self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to claim 3, characterized in that, In step S4, the process of selecting the optimal window length is as follows: For multiple preset window lengths, phase estimation and compensation are performed on the filtered data, the contrast of the compensated image is calculated, and the window length corresponding to the maximum contrast value is taken as the optimal window length for the current highlight area.

5. The self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to claim 4, characterized in that, In step S5, the formula for weighted phase gradient estimation is: in, For angle calculation symbols, For the conjugate of a vector or matrix, The target signal after discretization. To and The conjugate of discrete signals of adjacent azimuth units. Indicates the first The weights of each distance cell.

6. The self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to claim 5, characterized in that, No. The formula for the weight of each distance cell is: in, This represents the standard deviation of the current distance from the door. The amplitude of a pixel. This represents the average amplitude of the current distance from the gate.

7. The self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to claim 6, characterized in that, In step S6, the condition for iterative convergence is: The phase error change estimated between two consecutive iterations is less than a preset threshold, or the preset maximum number of iterations is reached.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the self-focusing motion compensation method based on sample cascade screening and local contrast weighting as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the steps of the self-focusing motion compensation method based on sample cascade screening and local contrast weighting according to any one of claims 1 to 7 by executing executable instructions.

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