Method and device for estimating sar error of large-angle strabismus, electronic equipment and storage medium

Abstract

The application discloses a large squint SAR error estimation method and device, electronic equipment and a storage medium, and belongs to the technical field of radar signal processing, and the method comprises the steps of: performing distance spectrum segmentation on a frequency domain echo signal of a to-be-measured target to obtain a steering vector of a plurality of sub-spectrum echo signals; performing eigenvalue decomposition on a covariance matrix between channels to obtain a noise subspace of each sub-spectrum; based on a local error estimation model, solving a channel error of each sub-spectrum according to the noise subspace of the sub-spectrum and the steering vector of the sub-spectrum echo signal; and fusing the channel error of the sub-spectrum to obtain a globally optimal channel error estimation value. The application effectively constrains the spectrum widening degree in each sub-spectrum by performing spectrum segmentation on the distance spectrum, and can solve the estimation misalignment problem caused by spectrum widening in the spaceborne large squint scene.

Description

Large-slant SAR error estimation methods and devices, electronic equipment, and storage media Technical Field

[0001] This invention belongs to the field of radar signal processing technology, specifically relating to a method and device for estimating large squint SAR error, electronic equipment, and storage medium. Background Technology

[0002] High resolution and wide swath imaging have always been the development goals of Synthetic Aperture Radar (SAR). However, the requirements for pulse repetition frequency are contradictory to the requirements for high resolution and wide swath. Azimuth multi-channel systems can overcome this bottleneck and are gradually becoming the mainstream solution in current SAR system design. To further expand the swath width and improve imaging flexibility, combining multi-channel technology with oblique-look modes has become a technological development direction that balances performance and application scenario adaptability. However, in practical engineering applications, errors often exist between channels, and the characteristics of Doppler spectrum broadening and center frequency variation with distance in large oblique-look scenarios exacerbate the coupling between errors and signals, significantly increasing the complexity of error processing and posing a severe challenge to high-precision imaging.

[0003] To address this, researchers have conducted extensive targeted studies on the mechanisms of strabismus imaging and multi-channel error estimation methods. They have not only achieved high-precision single-channel imaging in strabismus scenarios but also systematically analyzed and developed multi-channel error estimation techniques. Furthermore, they have proposed multi-channel SAR imaging schemes that integrate strabismus characteristics, such as imaging methods based on range-walking correction, ultimately achieving high-quality multi-channel imaging at small to medium strabismus angles. However, these methods still have significant technical limitations in complex scenarios with large strabismus angles and high aliasing rates. Traditional inter-channel error estimation methods are susceptible to the coupling effects of Doppler spectral broadening and spectral aliasing in large strabismus scenarios, leading to a significant decrease in error estimation accuracy and making it difficult to support high-precision imaging requirements. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for estimating large squint SAR errors, which can solve the problems of low estimation accuracy and large errors in traditional methods.

[0005] In a first aspect, embodiments of the present invention provide a large squint SAR error estimation method, the method comprising:

[0006] The frequency domain echo signal of the target under test is divided into range spectrum bands to obtain steering vectors of multiple sub-spectral band echo signals;

[0007] The covariance matrix between channels is decomposed into eigenvalues ​​to obtain the noise subspace of each sub-spectral band.

[0008] Based on the local error estimation model, the inter-channel error of each sub-spectral segment is solved according to the noise subspace of the sub-spectral segment and the steering vector of the echo signal of the sub-spectral segment.

[0009] By fusing the inter-channel errors of the sub-spectral bands, the globally optimal channel error estimate is obtained.

[0010] Secondly, embodiments of the present invention provide a large-slant-look SAR error estimation device, comprising:

[0011] The spectrum segmentation module is used to divide the frequency domain echo signal of the target under test into range spectrum segments to obtain steering vectors of multiple sub-spectral echo signals.

[0012] The feature decomposition module is used to perform feature decomposition on the covariance matrix between channels to obtain the noise subspace of each sub-spectral segment.

[0013] An error estimation module is used to solve the inter-channel error of each sub-spectral segment based on a local error estimation model, according to the noise subspace of the sub-spectral segment and the steering vector of the echo signal of the sub-spectral segment.

[0014] An error fusion module is used to fuse the inter-channel errors of sub-spectral bands to obtain the globally optimal channel error estimate.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a processor and a memory, wherein the memory is used to store a computer program; the processor can be used to execute a calculator program (instructions) stored in the memory to implement the method of the first aspect described above.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed, can implement the method described in the first aspect above.

[0017] The beneficial effects of this invention compared to existing technologies are as follows: Traditional range-walk correction methods introduce phase errors during correction. This invention estimates inter-channel errors by dividing the echo signal into range spectrum segments, instead of performing range-walk correction as in traditional methods, thus avoiding phase errors. Furthermore, this invention, through a range-oriented spectrum segmentation strategy, decomposes the globally complex and extended Doppler spectrum into multiple sub-segments, effectively constraining the spectral broadening within each sub-segment, resulting in more stable signal characteristics. This enables accurate estimation of inter-channel errors within a local area, solving the estimation inaccuracy problem caused by spectral broadening. In spaceborne large-squint scenarios, the ambiguity of multi-channel SAR satellites increases in the azimuth direction. Traditional methods of dividing the signal into subarrays in the spatial dimension or dividing the signal azimuth spectrum are no longer applicable. This invention utilizes the separability of the signal in the range-frequency domain to divide its range spectrum into segments and estimates the inter-channel errors of each sub-segment separately. This makes this method applicable to highly complex scenarios such as large-squint, expanding the applicability of the method and improving its anti-interference capability against spectral aliasing and Doppler broadening. Attached Figure Description

[0018] Figure 1 is a flowchart illustrating the implementation of a large squint SAR error estimation method provided in an embodiment of the present invention;

[0019] Figure 2 is a schematic diagram of a slant-view multi-channel spaceborne SAR geometric model provided in an embodiment of the present invention;

[0020] Figure 3 is a schematic diagram of a scenario for dividing a frequency domain echo signal into range spectrum bands according to an embodiment of the present invention;

[0021] Figure 4 is a schematic diagram of a large squint SAR error estimation device provided in an embodiment of the present invention;

[0022] Figures 5a, 5b, and 5c are schematic diagrams of imaging three point targets in a scene obtained by estimating the inter-channel error according to an embodiment of the present invention.

[0023] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0025] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0026] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0027] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0028] The method provided in this embodiment of the invention can be applied to spaceborne equipment such as those including a processor and SAR radar or those including a processor and radar signal receiver. This embodiment of the invention does not impose any restrictions on the specific type of spaceborne equipment.

[0029] Figure 1 shows a flowchart of a large-slant-look SAR error estimation method provided by an embodiment of the present invention. As an example and not a limitation, the method may include steps S101 to S105, which are described below.

[0030] S101 receives the echo signal and transforms the echo signal to the frequency domain to obtain the frequency domain echo signal of the target under test.

[0031] In one possible implementation, the received echo signal in the two-dimensional time domain can be transformed into the range and azimuth frequency domains to obtain the frequency domain echo signal of the target.

[0032] For example, the echo signal can be obtained from the signal emitted by the radar of the target under test.

[0033] In one example, referring to Figure 2, if the azimuth multichannel SAR (current position denoted as A1, beam center denoted as B1) along... The shaft at speed flight, After a certain time, it moves to position A1' (the beam center moves accordingly to position B1'). For SAR in Given the coordinates on the axis, the echo signal of the target (located at point P) in the two-dimensional time domain can satisfy the following formula:

[0034] ,

[0035] in, For radar number The echo signals of the target under test received by each channel in the two-dimensional time domain are shown in Figure 2. For direction, slow time, For the first The distance between the individual channel and the reference channel The distance between the starting position of the beam center and the target point P is [missing information]. For distance to fast time; , , respectively, are window functions for the range time-domain signal and the azimuth time-domain signal. The imaginary unit, Represented by natural constant An exponential function with base 0. For the first Phase error of each channel relative to the reference channel, To adjust the frequency, For the first The slant range of each channel relative to the target. At the speed of light, The center wavelength of the radar transmitted signal. Pi is the mathematical constant of a circle.

[0036] For example, It can be calculated using the following formula:

[0037] ,

[0038] See Figure 2 for details. The slant distance of the beam center. The angle of view is the beam center.

[0039] Specifically, echo data reception can be achieved through radar receiver hardware or by using signal processing software.

[0040] In one example, the frequency domain echo signal of the target under test can satisfy the following formula:

[0041] ,

[0042] in, For the first Frequency domain representation of the target echo received by each channel For range frequency, For azimuth frequency, For carrier frequency, , The frequency domain representations of the corresponding distance and azimuth window functions are shown in Figure 2. This represents the shortest slant distance from the radar's trajectory to the scene.

[0043] Specifically, a Fast Fourier Transform (FFT) hardware module or an FFT software algorithm can be used to perform a Fourier transform on the echo signal of the target in the two-dimensional time domain to transform the echo signal to the frequency domain.

[0044] S102, divide the frequency domain echo signal of the target under test into range spectrum segments to obtain the steering vectors of multiple sub-spectral echo signals.

[0045] In one possible implementation, it can be along the distance to the frequency. The frequency domain echo signal of the target under test is divided into multiple sub-spectral band echo signals.

[0046] For example, referring to Figure 3, a frequency domain echo signal with bandwidth B can be divided into three sub-spectral echo signals. The center of the middle sub-spectral echo signal is the same as the center of the original frequency domain echo signal. Overlapping. The range frequencies of the three sub-spectral bands are as follows: , , The azimuth frequencies are as follows: , , , The Doppler center is determined by strabismus, and the Doppler bandwidth of the sub-spectral echo signal is... .

[0047] In one example, the Doppler center of the sub-spectral echo signal can satisfy the following formula:

[0048] ,

[0049] in, For the first Doppler center of sub-spectral echo signal The total number of sub-segments. express Belongs to the set of integers .

[0050] In one example, the Doppler bandwidth of the divided sub-spectral echo signal should meet the following constraints: ,in, The pulse repetition frequency, Represents the speed of light. It is a slanted viewing angle; thus ensuring that the signal does not alias.

[0051] In one possible implementation, similar to the steering vector of the frequency domain echo signal, the steering vector of the sub-spectral echo signal can be determined by the Doppler center of the sub-spectral echo signal.

[0052] In one example, the diagonal matrix formed by the multi-channel phase errors of the frequency domain echo signal. It can be represented as: , guide vector matrix It can be represented as ,in, For the frequency domain echo signal, the first The steering vector of each fuzzy component, where L is a semi-fuzzy number, and ,in It is a fuzzy number.

[0053] Specifically:

[0054] ,

[0055] ,

[0056] in, For the first The fuzzy component is the first The guide vector of each channel, The Doppler center of the frequency domain echo signal. less than or equal to positive integers, This represents the total number of channels.

[0057] Therefore, the fuzzy number is The steering vector of the echo signal in each sub-spectral band can satisfy the following formula:

[0058] ,

[0059] in:

[0060] ,

[0061] ,

[0062] in, For the first The first of the sub-segments The fuzzy component is the first The guide vector of each channel, For the first The steering vector of the sub-spectral echo signal.

[0063] S103, perform eigenvalue decomposition on the covariance matrix between channels to obtain the noise subspace of each sub-spectral segment.

[0064] In one possible implementation, the covariance matrix between channels can be determined based on the frequency domain echo signal, and then the covariance matrix can be eigenvalued to obtain the noise subspace of each sub-spectral band.

[0065] In one example, the covariance matrix can satisfy the following formula:

[0066] ,

[0067] in, Let covariance matrix be the variance matrix. This is the symbol for calculating the mean. Indicates transpose. For the first The steering vector matrix of each sub-spectral segment .in For the frequency domain representation of the echo signal, It is a vector formed by the echoes corresponding to all fuzzy components.

[0068] S104, based on the local error estimation model, solves the inter-channel error of each sub-spectral segment according to the noise subspace of the sub-spectral segment and the steering vector of the echo signal of the sub-spectral segment.

[0069] In one possible implementation, the local error estimation model can be constructed based on the orthogonality between the steering vector of the sub-spectral echo signal and the noise subspace of the sub-spectral segment.

[0070] For example, in the local error estimation model, the first... The objective function corresponding to each sub-spectral segment The following formula can be satisfied:

[0071] ,

[0072] in, For the first The noise subspace of each sub-spectral band This indicates taking the modulus.

[0073] In one example, to achieve error estimation within each sub-spectral band, the local error estimation model minimizes... With the objective of (i.e., the inner product of the steering vector of the sub-spectral echo signal and the noise subspace of the sub-spectral segment), the error estimation problem is transformed into solving the following optimization problem:

[0074] ,

[0075] in, , The column vector operator represents the diagonal vectors of a matrix. , This indicates the construction of a diagonal matrix. For the first The diagonal matrix of the steering vectors corresponding to each fuzzy component.

[0076] Ultimately, by solving the objective... The linear constraint is ( The local error estimation model can be used to obtain the inter-channel error of each sub-spectral band.

[0077] In one example, the solved inter-channel error of the sub-spectral band can satisfy the following formula:

[0078] ,

[0079] in:

[0080] ,

[0081] in, For the first Inter-channel error of each sub-spectral band For the first Channel error covariance matrix of each sub-spectral band Define the symbol.

[0082] Optionally, the inter-channel error estimation process for multiple sub-spectral bands can be performed synchronously in multiple parallel channels to improve computational efficiency.

[0083] S105, the inter-channel errors of the fused sub-spectral bands are used to obtain the globally optimal channel error estimate.

[0084] In one possible implementation, it can be derived from... The inter-channel errors of any number of sub-spectral segments are selected from each sub-spectral segment and fused to obtain the globally optimal channel error estimate.

[0085] By flexibly selecting the number of fused spectral bands within the constraints, it is possible to ensure that the estimation accuracy is maintained without a significant increase in computational cost.

[0086] In one example, the mean method or the least squares method can be used to fuse the inter-channel errors of the selected sub-spectral bands.

[0087] Traditional range-walk correction methods introduce phase errors during correction. This invention estimates inter-channel errors by dividing the echo signal into range spectral segments, instead of performing range-walk correction as in traditional methods, thus avoiding phase errors. Furthermore, this invention employs a range-oriented spectrum segmentation strategy to decompose the globally complex Doppler spectrum into multiple sub-segments, effectively constraining the spectral broadening within each sub-segment and resulting in more stable signal characteristics. This enables accurate estimation of inter-channel errors within a local area, resolving estimation inaccuracies caused by spectral broadening. In spaceborne large-slant-look scenarios, the ambiguity of multi-channel SAR satellites increases in the azimuth direction. Traditional methods of spatial subarray division or azimuth spectrum segmentation are no longer applicable. This invention utilizes the separability of the signal in the range-frequency domain to divide the range spectral segment and estimates the inter-channel error of each sub-segment separately. This makes the method applicable to highly complex scenarios such as large-slant-look scenarios, expanding its applicability and improving its resistance to spectral aliasing and Doppler broadening.

[0088] Furthermore, after dividing the spectrum into segments, the error estimation of each sub-segment can be performed independently and synchronously, which can significantly improve computational efficiency and better meet the needs of engineering applications.

[0089] Figure 4 shows a schematic diagram of a large squint SAR error estimation device provided in an embodiment of the present invention. As an example and not a limitation, the device may include a spectral segmentation module, a feature decomposition module, an error estimation module, and an error fusion module.

[0090] For example, the spectrum segmentation module is used to segment the frequency domain echo signal of the target under test into range spectrum segments to obtain steering vectors of multiple sub-spectral echo signals; the eigenvalue decomposition module is used to perform eigenvalue decomposition on the covariance matrix between channels to obtain the noise subspace of each sub-spectral segment; the error estimation module is used to solve the inter-channel error of each sub-spectral segment based on the local error estimation model, according to the noise subspace of the sub-spectral segment and the steering vector of the sub-spectral echo signal; and the error fusion module is used to fuse the inter-channel errors of the sub-spectral segments to obtain the globally optimal channel error estimate.

[0091] To better illustrate the beneficial effects of the present invention, the following simulation experiments were conducted:

[0092] For example, the simulation experiment can be conducted with the following conditions: CPU: Intel Xeon E5-2643 v3 @ 3.40GHz six-core (X2); Memory: 128.00 GB; Operating System: Windows 10; Simulation Software: MATLAB R2022a. The specific values ​​of each parameter are shown in Table 1 below:

[0093] Table 1 Simulation Parameters

[0094]

[0095] In the simulation experiment, the phase errors added to each channel of the radar are shown in Table 2 below:

[0096] Table 2 Simulation Error

[0097]

[0098] The phase errors of each channel estimated by the method provided by the present invention are shown in Table 3 below:

[0099] Table 3 Estimation Error

[0100]

[0101] To quantify the effectiveness of error estimation, this invention uses the root mean square relative error between the estimated value and the actual value for evaluation.

[0102] For example, the formula for calculating the root mean square relative error is as follows:

[0103] ,

[0104] in, , They represent the first The actual phase error and the estimated phase error of each channel This represents the root mean square relative error between the two, where the total number of channels is [value missing]. =19.

[0105] The root mean square relative error calculated using the present invention, as shown in Tables 2 and 3 above, is 0.0288°. This indicates that the estimation error of the method provided by the present invention is relatively close to the actual error, meeting the application standards.

[0106] Figures 5a, 5b, and 5c show the imaging results of three point targets in the scene after the signal is compensated for by the inter-channel error estimated by the method provided by the present invention. As can be seen from Figures 5a-5c, the point targets are well focused after error compensation, proving that the present invention can achieve accurate estimation of inter-channel error in spaceborne large squint scenes.

[0107] Therefore, this invention estimates inter-channel errors by dividing the echo signal into range spectrum segments, rather than performing range travel correction as in traditional methods, thus avoiding phase errors. Furthermore, this invention, through a range-oriented spectrum segmentation strategy, decomposes the globally complex and extended Doppler spectrum into multiple sub-segments, effectively constraining the spectral broadening within each sub-segment and resulting in more stable signal characteristics. This enables accurate estimation of inter-channel errors within a local area, resolving estimation inaccuracies caused by spectral broadening. In spaceborne large-slant-out scenarios, the ambiguity of multi-channel SAR satellites increases in the azimuth direction. Traditional methods of spatial subarray division or azimuth spectrum segmentation are no longer applicable. This invention utilizes the separability of the signal in the range-frequency domain to divide the range spectrum and estimate the inter-channel error of each sub-segment separately. This makes the method applicable to highly complex scenarios such as large-slant-out scenarios, expanding its applicability and improving its resistance to spectral aliasing and Doppler broadening.

[0108] Figure 6 shows a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 600 shown in Figure 6 may include: at least one processor 610 (only one processor is shown in Figure 6), a memory 620, and a computer program 630 stored in the memory 620 and executable on the at least one processor 610. When the processor 610 executes the computer program 630, it implements the steps in any of the above-described method embodiments.

[0109] The electronic device 600 may be a robot or other processing device capable of implementing the above methods. This embodiment of the invention does not impose any restrictions on the specific type of electronic device.

[0110] Those skilled in the art will understand that Figure 6 is merely an example of electronic device 600 and does not constitute a limitation on the electronic device. It may include more or fewer components than shown, or combine certain components, or use different components. For example, the electronic device 600 may also include input / output interfaces.

[0111] The processor 610 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASTCs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0112] In some embodiments, the memory 620 may be an internal storage unit, such as a hard disk or RAM. In other embodiments, the memory 620 may be an external storage device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD), or a flash card. Furthermore, the memory 620 may include both internal and external storage units. The memory 620 is used to store the operating system, applications, a boot loader, data, and other programs, such as the program code of the computer program. The memory 620 can also be used to temporarily store data that has been output or will be output.

[0113] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0114] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0115] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0116] This invention provides a computer program product that, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments above.

[0117] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0118] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0119] 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 implementations should not be considered beyond the scope of this invention.

Claims

1. A method for estimating large-slant-look SAR error, characterized in that, include: The frequency domain echo signal of the target under test is divided into range spectrum bands to obtain steering vectors of multiple sub-spectral band echo signals; The covariance matrix between channels is decomposed using eigenvalues ​​to obtain the noise subspace of each sub-spectral segment. Based on the local error estimation model, the inter-channel error of each sub-spectral segment is solved according to the noise subspace of the sub-spectral segment and the steering vector of the echo signal of the sub-spectral segment. The inter-channel errors of the sub-spectral segments are fused to obtain the globally optimal channel error estimate. The Doppler center of the echo signal of the sub-spectral segment satisfies the following formula: ,in, For the first Doppler center of sub-spectral echo signal The constant Doppler center caused by strabismus. For carrier frequency, The total number of sub-segments. express Belongs to the set of integers ， Let be the Doppler bandwidth of the sub-spectral band echo signal; wherein, the local error estimation model is constructed based on the orthogonality between the steering vector of the sub-spectral band echo signal and the noise subspace of the sub-spectral band, and the local error estimation model aims to minimize the inner product of the steering vector of the sub-spectral band echo signal and the noise subspace of the sub-spectral band; wherein, the local error estimation model satisfies the following formula: ,in, Indicates minimization. This is a diagonal matrix composed of phase errors from multiple channels. In the local error estimation model, the first... The optimization objective for each sub-spectral segment For the first Inter-channel error of each sub-spectral band ， This indicates the construction of a diagonal matrix. For the first The first sub-spectral band echo signal The steering vector of the fuzzy component, For the error space, Indicates transpose. It is a semi-fuzzy number, and ,in Let be the ambiguity number; wherein, the inter-channel error of the sub-spectral segment satisfies the following formula: ,in: ,in, For the first Inter-channel error of each sub-spectral band For the first Channel error covariance matrix of each sub-spectral band ， This indicates the construction of a diagonal matrix. For the first The first sub-spectral band echo signal The steering vector of the fuzzy component, For the error space, Indicates transpose. It is a semi-fuzzy number, and ,in For fuzzy numbers, To define symbols, ， This represents the total number of channels.

2. The large squint SAR error estimation method according to claim 1, characterized in that, The Doppler bandwidth of the sub-spectral band echo signal satisfies the following formula: ,in, The Doppler bandwidth of the echo signal in the sub-spectral band is given. The pulse repetition frequency, Represents the speed of light. It is an oblique perspective. For the radar's movement speed, For carrier frequency.

3. The large squint SAR error estimation method according to claim 1, characterized in that, The inter-channel error of the fused sub-spectral segment is obtained to obtain the globally optimal channel error estimate, including: fusing the inter-channel error of the sub-spectral segment using the mean method or the least squares method to obtain the globally optimal channel error estimate.

4. A large-slant-look SAR error estimation device, characterized in that, include: The spectrum segmentation module is used to divide the frequency domain echo signal of the target under test into range spectrum segments to obtain steering vectors of multiple sub-spectral echo signals. The feature decomposition module is used to perform feature decomposition on the covariance matrix between channels to obtain the noise subspace of each sub-spectral segment. An error estimation module is used to calculate the inter-channel error of each sub-spectral segment based on a local error estimation model, according to the noise subspace of the sub-spectral segment and the steering vector of the echo signal of the sub-spectral segment; an error fusion module is used to fuse the inter-channel errors of the sub-spectral segments to obtain the globally optimal channel error estimate; wherein, the Doppler center of the echo signal of the sub-spectral segment satisfies the following formula: ,in, For the first Doppler center of sub-spectral echo signal The constant Doppler center caused by strabismus. For carrier frequency, The total number of sub-segments. express Belongs to the set of integers ， Let be the Doppler bandwidth of the sub-spectral band echo signal; wherein, the local error estimation model is constructed based on the orthogonality between the steering vector of the sub-spectral band echo signal and the noise subspace of the sub-spectral band, and the local error estimation model aims to minimize the inner product of the steering vector of the sub-spectral band echo signal and the noise subspace of the sub-spectral band; wherein, the local error estimation model satisfies the following formula: ,in, Indicates minimization. This is a diagonal matrix composed of phase errors from multiple channels. In the local error estimation model, the first... The optimization objective for each sub-spectral segment For the first Inter-channel error of each sub-spectral band ， This indicates the construction of a diagonal matrix. For the first The first sub-spectral band echo signal The steering vector of the fuzzy component, For the error space, Indicates transpose. It is a semi-fuzzy number, and ,in Let be the ambiguity number; wherein, the inter-channel error of the sub-spectral segment satisfies the following formula: ,in: ,in, For the first Inter-channel error of each sub-spectral band For the first Channel error covariance matrix of each sub-spectral band ， This indicates the construction of a diagonal matrix. For the first The first sub-spectral band echo signal The steering vector of the fuzzy component, For the error space, Indicates transpose. It is a semi-fuzzy number, and ,in For fuzzy numbers, To define symbols, ， This represents the total number of channels.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-3.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by an electronic device, it implements the method as described in any one of claims 1-3.

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