Large-rotation-angle sliding spotlight SAR speed space-variant precision processing method

Abstract

The invention relates to a large-rotation-angle sliding bunching SAR speed space-variant precision processing method, which comprises the steps of performing large-rotation-angle sliding bunching SAR data imaging by utilizing a sliding bunching two-step imaging algorithm, then performing two-dimensional partitioning on an SAR image, then performing secondary phase error estimation by utilizing an MD algorithm, and finally, performing two-dimensional phase error estimation by utilizing the MD algorithm. And calculating a residual speed error corresponding to the secondary phase error as a block center speed error, calculating to obtain an actual value of a two-dimensional space-variant speed, and finally performing imaging processing by using a new speed to achieve an effect of speed space-variant fine processing. The problem of image defocusing caused by residual space-variant speed errors under the condition that high-resolution large-rotation-angle sliding bunching imaging lacks precise orbit determination data or the orbit parameter precision is not enough can be effectively solved, and therefore high-quality imaging is achieved.

Description

Technical Field

[0001] This application relates to the field of space microwave radar signal processing, specifically to a method for fine processing of velocity spatial variation in large-angle sliding spotting SAR. Background Technology

[0002] The main development direction of spaceborne SAR is high resolution and wide swath. The sliding beamforming mode, by changing the beam direction, increases the integration time (synthetic aperture time) of the target, achieving high-resolution imaging, and has been widely used in spaceborne SAR. To achieve decimeter-level resolution, spaceborne SAR can operate at beam rotation angles as high as ±15°. At such large rotation angles, the slant range history from the orbital position to the target no longer satisfies the hyperbolic model. The spatial variation of the target's imaging parameters along the range and azimuth directions is very significant across the entire scene. In particular, the azimuth phase error and range migration error caused by the equivalent velocity change of the target severely affect the target focusing effect, leading to image quality degradation. Existing technologies, based on traditional sliding beamforming imaging algorithms, compensate for spatially varied velocity errors through motion error compensation. To obtain high-precision compensation results, high-precision orbital parameters are required. However, without precise orbit determination data or with insufficient orbital parameter accuracy, high-quality imaging cannot be achieved. Summary of the Invention

[0003] To overcome at least one deficiency in the prior art, this application provides a method for fine processing of velocity space variation in large-angle sliding focused SAR.

[0004] Firstly, a method for fine-tuning velocity space-varying SAR with large-angle sliding focus is provided, including: The sliding spotting SAR echo data with large rotation angle is processed by the two-step imaging algorithm to obtain SAR images. Perform an inverse Fourier transform on the SAR image to transform the image to the azimuth time domain before focusing, and obtain the unfocused azimuth time domain signal. The non-focused azimuth time-domain signal is divided into two-dimensional blocks to obtain multiple sub-blocks; Perform a second phase error estimation on each sub-block to obtain the phase error of each sub-block; The phase error of each sub-block is fitted twice to obtain the quadratic phase error coefficient of each sub-block; Calculate the center residual velocity error of each sub-block based on the quadratic phase error coefficient of each sub-block; Based on the residual velocity error at the center of each sub-block, the actual velocity at the center of each sub-block is calculated; the actual velocity at the center of each sub-block is then resampled in two dimensions using spline interpolation to obtain the actual value of the spatially varying velocity corresponding to the SAR echo data. A two-step sliding beamforming imaging algorithm is adopted to perform imaging processing based on the actual value of the space-varying velocity, resulting in a SAR image with finely processed space-varying velocity.

[0005] In one embodiment, the non-focused azimuth time-domain signal is divided into two-dimensional blocks, and the block division criteria include: azimuth block division criteria and range block division criteria. The azimuth partitioning criteria are:

[0006] in, For azimuth-oriented block time, This is the distance from the gate's slant distance. The equivalent velocity corresponding to the distance gate. Wavelength; The distance-oriented partitioning criterion is:

[0007] in, The distance is the slant distance of the block. To find the minimum value function, For azimuth synthesis aperture time, For range resolution, This is the distance from the gate's slant distance.

[0008] In one embodiment, the center residual velocity error of each sub-block is calculated based on the quadratic phase error coefficient of each sub-block using the following formula:

[0009]

[0010]

[0011] in, The center residual velocity error of the sub-block. For wavelength, It is a cosine function. The distance from the center of the sub-block to the door's slant distance. The equivalent velocity of the distance from the center of the sub-block to the gate. This is the oblique angle of the azimuth center. The quadratic phase error coefficient of the sub-block; This is the azimuth scale scaling factor in the sliding beam two-step imaging algorithm; The azimuth scale scaling and frequency modulation slope of the azimuth signal after derotation frequency modulation are given in the sliding beam two-step imaging algorithm. The distance from the center of the echo window to the equivalent velocity of the door. The reference slant distance for scale variation; The slant distance for beam center rotation.

[0012] In one embodiment, the center residual velocity error of each sub-block is calculated based on the quadratic phase error coefficient of each sub-block using the following formula:

[0013] in, The center residual velocity error of the sub-block. The center residual velocity error of the sub-block. The equivalent velocity of the gate is the distance from the center of the sub-block.

[0014] In one embodiment, the azimuth time of two-dimensional resampling and slant distance They are respectively:

[0015] in, For the azimuth points of the echo data, The time is the center of the azimuth. For the azimuth points of the echo data, This is the azimuth index number for the echo data. For the azimuth repetition rate of echo data, This is the gate start time. For the range index number of the echo data, At the speed of light, For the distance sampling rate, This represents the number of distance points in the echo data after distance pulse compression.

[0016] Secondly, a large-angle sliding spotting SAR velocity space-variable fine processing device is provided, comprising: The first imaging module is used to perform imaging processing on the large-angle sliding spotting SAR echo data based on the two-step sliding spotting imaging algorithm to obtain SAR images. The inverse Fourier transform module is used to perform azimuth-to-inverse Fourier transform on SAR images, transforming the images to the azimuth-time domain before focusing, and obtaining the unfocused azimuth-time domain signal. The two-dimensional block module is used to perform two-dimensional block division on the non-focused azimuth time domain signal to obtain multiple sub-blocks; The phase error estimation module is used to perform a secondary phase error estimation on each sub-block to obtain the phase error of each sub-block; The fitting module is used to perform a second-order fitting on the phase error of each sub-block to obtain the second-order phase error coefficient of each sub-block. The center residual velocity error calculation module is used to calculate the center residual velocity error of each sub-block based on the quadratic phase error coefficient of each sub-block. The actual value determination module for spatially varying velocity is used to calculate the actual center velocity of each sub-block based on the residual velocity error at the center of each sub-block; and to perform two-dimensional resampling of the actual center velocity of each sub-block through spline interpolation to obtain the actual value of spatially varying velocity corresponding to the SAR echo data. The second imaging module is used to perform imaging processing based on the actual value of the space-varying velocity using a sliding beam two-step imaging algorithm, and obtain a SAR image with finely processed space-varying velocity.

[0017] Compared with the prior art, this application has the following beneficial effects: The large-angle sliding spot SAR velocity spatial variation fine processing method of this application determines the analytical expression of the residual velocity error corresponding to the second phase error of the image under the sliding spot two-step imaging algorithm, and achieves the effect of velocity spatial variation fine processing. It can effectively solve the image defocusing problem caused by residual spatial variation velocity error in high-resolution large-angle sliding spot imaging when there is a lack of precise orbit determination data or insufficient orbit parameter accuracy, thereby achieving high-quality imaging. Attached Figure Description

[0018] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which, together with the detailed description below, are incorporated in and form part of this specification. In the drawings: Figure 1 A flowchart of a method for fine-tuning velocity spatial variation in large-angle sliding focus SAR is shown. Detailed Implementation

[0019] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.

[0020] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution of this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0021] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.

[0022] This application provides a method for fine-tuning the velocity space-variable of large-angle sliding focus SAR. Figure 1A flowchart of a high-angle sliding focus SAR velocity space-variable fine processing method is shown. See [link / reference]. Figure 1 The method mainly includes the following steps: Step S1: Based on the two-step sliding spotting imaging algorithm, the large-angle sliding spotting SAR echo data is processed to obtain a SAR image.

[0023] The sliding-focused two-step imaging algorithm is a method for processing echo data in sliding-focused mode, aiming to achieve high-resolution imaging. Existing sliding-focused two-step imaging algorithms incorporating motion compensation can also be employed here.

[0024] Step S2: Perform an inverse fast fourier transform (FFT) on the SAR image to transform the image to the azimuth time domain before focusing, and obtain the unfocused azimuth time domain signal.

[0025] Step S3: Divide the non-focused azimuth time domain signal into two-dimensional blocks to obtain multiple sub-blocks.

[0026] Specifically, the partitioning criteria adopted include: azimuth partitioning criteria and range partitioning criteria; The azimuth partitioning criteria are:

[0027] in, For azimuth-oriented block time, This is the distance from the gate's slant distance. The equivalent velocity corresponding to the distance gate. Wavelength; The distance-oriented partitioning criterion is:

[0028] in, The distance is the slant distance of the block. To find the minimum value function, For azimuth synthesis aperture time, For range resolution, This is the distance from the gate's slant distance.

[0029] Step S4: Perform a second phase error estimation for each sub-block to obtain the phase error of each sub-block. ,in This represents the location and time corresponding to the sub-block. This is the distance sub-block index number. This is the index number of the azimuth sub-block.

[0030] Here, the classic MD estimation algorithm (MapDrift algorithm) can be used to estimate the secondary phase error.

[0031] Step S5: Perform a second-order fitting on the phase error of each sub-block to obtain the second-order phase error coefficient of each sub-block.

[0032] Here, a higher-order fitting function is used to perform a second fitting on the phase error of each sub-block.

[0033] Step S6: Calculate the center residual velocity error of each sub-block based on the quadratic phase error coefficient of each sub-block.

[0034] Specifically, the following formula is used:

[0035]

[0036]

[0037] in, The center residual velocity error of the sub-block. For wavelength, It is a cosine function. The distance from the center of the sub-block to the door's slant distance. The equivalent velocity of the distance from the center of the sub-block to the gate. This is the oblique angle of the azimuth center. The quadratic phase error coefficient of the sub-block; This is the azimuth scale scaling factor in the sliding beam two-step imaging algorithm; The azimuth scale scaling and frequency modulation slope of the azimuth signal after derotation frequency modulation are given in the sliding beam two-step imaging algorithm. The distance from the center of the echo window to the equivalent velocity of the door. The reference slant distance for scale variation; The slant distance for beam center rotation.

[0038] Step S7: Calculate the actual center velocity of each sub-block based on the residual velocity error at the center of each sub-block; perform two-dimensional resampling of the actual center velocity of each sub-block using spline interpolation to obtain the actual spatial velocity value corresponding to the SAR echo data.

[0039] Specifically, the center residual velocity error of each sub-block is calculated using the following formula:

[0040] in, The center residual velocity error of the sub-block. The center residual velocity error of the sub-block. The equivalent velocity of the gate is the distance from the center of the sub-block.

[0041] Here, the actual value of the spatial velocity corresponding to the SAR echo data. ,in, This is the azimuth index number for the echo data. This is the range index number for the echo data.

[0042] The azimuth time of the center of each sub-block for estimating the residual velocity error and slant distance They are respectively:

[0043] in, This is the number of azimuth points after two-step defuzzification. For the azimuth repetition rate of echo data, The time is the center of the azimuth. This is the gate start time. At the speed of light, For the distance sampling rate, This is the center orientation index of the block. This is the block center distance index.

[0044] azimuth time of two-dimensional resampling and slant distance They are respectively:

[0045] in, For the azimuth points of the echo data, The time is the center of the azimuth. For the azimuth points of the echo data, This is the azimuth index number for the echo data. For the azimuth repetition rate of echo data, This is the gate start time. For the range index number of the echo data, At the speed of light, For the distance sampling rate, This represents the number of distance points in the echo data after distance pulse compression.

[0046] Step S8: Using the sliding beam two-step imaging algorithm, imaging processing is performed based on the actual value of the space-varying velocity to obtain a SAR image with finely processed space-varying velocity.

[0047] This application defines an analytical expression for the residual velocity error corresponding to the secondary phase error of the image under the two-step sliding beamforming imaging algorithm, achieving the effect of fine processing of velocity spatial variation. It can effectively solve the image defocusing problem caused by residual spatial variation velocity error in high-resolution large-angle sliding beamforming imaging when there is a lack of precise orbit determination data or insufficient accuracy of orbit parameters, thereby achieving high-quality imaging.

[0048] Based on the same inventive concept as the large-angle sliding spotlight SAR velocity space-variable fine processing method, this embodiment also provides a corresponding large-angle sliding spotlight SAR velocity space-variable fine processing device, including: The inverse Fourier transform module is used to perform azimuth-to-inverse Fourier transform on SAR images, transforming the images to the azimuth-time domain before focusing, and obtaining the unfocused azimuth-time domain signal. The two-dimensional block module is used to perform two-dimensional block division on the non-focused azimuth time domain signal to obtain multiple sub-blocks; The phase error estimation module is used to perform a secondary phase error estimation on each sub-block to obtain the phase error of each sub-block; The fitting module is used to perform a second-order fitting on the phase error of each sub-block to obtain the second-order phase error coefficient of each sub-block. The center residual velocity error calculation module is used to calculate the center residual velocity error of each sub-block based on the quadratic phase error coefficient of each sub-block. The actual value determination module for spatially varying velocity is used to calculate the actual center velocity of each sub-block based on the residual velocity error at the center of each sub-block; and to perform two-dimensional resampling of the actual center velocity of each sub-block through spline interpolation to obtain the actual value of spatially varying velocity corresponding to the SAR echo data. The second imaging module is used to perform imaging processing based on the actual value of the space-varying velocity using a sliding beam two-step imaging algorithm, and obtain a SAR image with finely processed space-varying velocity.

[0049] The large-angle sliding spot SAR velocity space-variable fine processing device of this embodiment has the same inventive concept as the large-angle sliding spot SAR velocity space-variable fine processing method described above. Therefore, the specific implementation of this device can be found in the embodiment section of the large-angle sliding spot SAR velocity space-variable fine processing method described above, and its technical effects correspond to the technical effects of the above method, so it will not be repeated here.

[0050] The above descriptions are merely various embodiments of this application, but 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. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for fine processing of velocity space-variable parameters in large-angle sliding focus SAR, characterized in that, include: The sliding spotting SAR echo data with large rotation angle is processed by the two-step imaging algorithm to obtain SAR images. Perform an inverse Fourier transform on the SAR image to transform the image to the azimuth time domain before focusing, and obtain the unfocused azimuth time domain signal. The non-focused azimuth time-domain signal is divided into two-dimensional blocks to obtain multiple sub-blocks; Perform a second phase error estimation on each sub-block to obtain the phase error of each sub-block; The phase error of each sub-block is fitted twice to obtain the quadratic phase error coefficient of each sub-block; The center residual velocity error of each sub-block is calculated based on the quadratic phase error coefficient of each sub-block. Based on the center residual velocity error of each sub-block, the center actual velocity of each sub-block is calculated; the center actual velocity of each sub-block is resampled in two dimensions by spline interpolation to obtain the actual value of the spatially varying velocity corresponding to the SAR echo data. A two-step sliding beamforming imaging algorithm is used to perform imaging processing based on the actual value of the space-varying velocity, resulting in a SAR image with finely processed space-varying velocity.

2. The method as described in claim 1, characterized in that, in, The non-focused azimuth time-domain signal is divided into two-dimensional blocks, and the block division criteria include: azimuth block division criteria and range block division criteria. The orientation-based block segmentation criterion is as follows: in, For azimuth-oriented block time, This is the distance from the gate's slant distance. The equivalent velocity corresponding to the distance gate. Wavelength; The distance-oriented block partitioning criterion is as follows: in, The distance is the slant distance of the block. To find the minimum value function, For azimuth synthesis aperture time, For range resolution, This is the distance from the gate's slant distance.

3. The method as described in claim 1, characterized in that, in, The center residual velocity error of each sub-block is calculated based on the quadratic phase error coefficient of each sub-block using the following formula: in, The center residual velocity error of the sub-block. For wavelength, It is a cosine function. The distance from the center of the sub-block to the door's slant distance. The equivalent velocity of the distance from the center of the sub-block to the gate. This is the oblique angle of the azimuth center. The quadratic phase error coefficient of the sub-block; This is the azimuth scale scaling factor in the sliding beam two-step imaging algorithm; The azimuth scale scaling and frequency modulation slope of the azimuth signal after derotation frequency modulation are given in the sliding beam two-step imaging algorithm. The distance from the center of the echo window to the equivalent velocity of the door. The reference slope distance for scale variation; The slant distance for beam center rotation.

4. The method as described in claim 1, characterized in that, in, The center residual velocity error of each sub-block is calculated based on the quadratic phase error coefficient of each sub-block using the following formula: in, The center residual velocity error of the sub-block. The center residual velocity error of the sub-block. The equivalent velocity of the gate is the distance from the center of the sub-block.

5. The method as described in claim 1, characterized in that, The azimuth time of the two-dimensional resampling and slant distance They are respectively: in, For the azimuth points of the echo data, The time is the center of the azimuth. For the azimuth points of the echo data, This is the azimuth index number for the echo data. For the azimuth repetition rate of echo data, This is the gate start time. For the range index number of the echo data, At the speed of light, For the distance sampling rate, This represents the number of distance points in the echo data after distance pulse compression.

6. A large-angle sliding focus SAR velocity space-varying fine processing device, characterized in that, include: The first imaging module is used to perform imaging processing on the large-angle sliding spotting SAR echo data based on the two-step sliding spotting imaging algorithm to obtain SAR images. The inverse Fourier transform module is used to perform an azimuth-directed inverse Fourier transform on the SAR image, transforming the image to the azimuth-time domain before focusing, and obtaining an unfocused azimuth-time domain signal. A two-dimensional segmentation module is used to perform two-dimensional segmentation on the non-focused azimuth time-domain signal to obtain multiple sub-blocks; The phase error estimation module is used to perform a secondary phase error estimation on each sub-block to obtain the phase error of each sub-block; The fitting module is used to perform a second-order fitting on the phase error of each sub-block to obtain the second-order phase error coefficient of each sub-block. The center residual velocity error calculation module is used to calculate the center residual velocity error of each sub-block based on the quadratic phase error coefficient of each sub-block. The actual value determination module for spatial velocity is used to calculate the actual center velocity of each sub-block based on the residual velocity error at the center of each sub-block; and to perform two-dimensional resampling of the actual center velocity of each sub-block through spline interpolation to obtain the actual value of spatial velocity corresponding to the SAR echo data. The second imaging module is used to perform imaging processing based on the actual value of the space-varying velocity using a sliding beam two-step imaging algorithm to obtain a SAR image with finely processed space-varying velocity.

Images