A RAA residual space variant error compensation method suitable for a curve trajectory SAR

Abstract

The application provides a RAA residual error compensation method suitable for a curve trajectory SAR, comprising: using a radius angle interpolation algorithm to perform imaging processing on obtained SAR echo data, using a new residual error modeling method to perform error modeling, and designing a residual error compensation method to perform phase error compensation to obtain a final SAR image. The radius angle interpolation algorithm is introduced into airborne curve trajectory large forward-looking SAR imaging, and under the condition of large forward-looking, there is a large approximation in the derivation process of the algorithm, there is a large residual phase error for scene edge point targets, and the imaging quality of image edge points is poor. The application compensates the residual phase error of the initial imaging result by constructing a residual phase error space model, improves the focusing depth of the algorithm under the condition of the curve trajectory large forward-looking, expands the effective imaging scene, and greatly improves the limitation of the residual error on the large forward-looking and large scene imaging.

Description

Technical Field

[0001] This invention belongs to the field of radar imaging technology, specifically relating to a method for compensating for residual spatial variation error in RAA (Radar Acoustic Alternation) applicable to curved trajectory SAR. Background Technology

[0002] Synthetic Aperture Radar (SAR) uses electromagnetic waves in the microwave spectrum as its detection medium to observe surface features, offering advantages such as all-weather, all-time operation, strong penetration, multi-functionality, and versatility. Its two-dimensional high resolution has led to its widespread application in deep space exploration, topographic mapping, maritime search and rescue, and autonomous driving. However, in recent years, due to changing practical needs, the motion state of SAR is no longer limited to simple forward-looking uniform linear motion. Increasingly, application scenarios are placing new demands on SAR imaging technology, such as curved trajectories, high resolution, and large forward-looking angles. To meet the requirements of aircraft safety and wide-area imaging, more and more SAR systems are required to operate in curved trajectory and large forward-looking angle environments. However, the nonlinear characteristics of the motion trajectory and the large forward-looking angle lead to large approximation errors and insufficient accuracy in existing algorithms, thus limiting the effective scene size of SAR imaging and posing new challenges to large forward-looking angle imaging. Therefore, researching SAR imaging technology applicable to high-resolution, wide-swath conditions with large forward-looking angles has significant theoretical importance, practical value, and real-world needs.

[0003] Existing Solution: Xi'an University of Electronic Science and Technology disclosed a SAR imaging method for hypersonic maneuvering platforms based on radius-angle interpolation in its patent application "SAR Imaging Method for Hypersonic Maneuvering Platforms Based on Radius-Angle Interpolation" (Application No. 201910815843.X, Publication No. CN110515080A). This method performs range pulse compression and consistent phase compensation processing on the radar echo signal, performs two-dimensional radius-angle interpolation on the range-frequency domain and azimuth-time domain signals, and finally obtains a two-dimensional focused image through two-dimensional Fourier transform. This method has good applicability and is simple to operate, and can overcome the shortcomings of existing technologies in terms of poor focusing accuracy in non-ideal planar imaging scenes. However, under the condition of large forward-looking high-resolution wide swath, this method has a large approximation when unfolding the slant range history of the platform, resulting in a large residual phase error at the edge points and poor focusing effect. This leads to defocusing of targets far from the center point of the scene in the imaging result, limiting the imaging swath. In severe cases, this method may even fail to image, resulting in a small effective imaging scene. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a residual spatial variation error compensation method for RAA (Rapid Alternating Aspect) applicable to curved trajectory SAR. The technical problem to be solved by this invention is achieved through the following technical solution:

[0005] This invention provides a method for compensating for residual spatial variation error in RAA (Rapid Alternating Aspect) of curved trajectory SAR, comprising:

[0006] S100: Acquire high-resolution SAR echo data with large forward-looking angle, and use a radius-angle interpolation algorithm to perform imaging processing on the SAR echo data to obtain the initial imaging result.

[0007] S200, a new method of time-frequency mapping is used to construct a spatial model of the residual phase error;

[0008] S300: Using the residual phase error spatial domain model of S200, phase error compensation is performed on the effective imaging region block in the initial imaging result to obtain the final SAR image.

[0009] Beneficial effects:

[0010] This invention provides a residual spatial variation error compensation method for RAA (Rapid Aspect-Adjusted Error) applicable to curved trajectory SAR, comprising: acquiring high-resolution SAR echo data with large forward squint, and performing imaging processing on the SAR echo data using a radius-angle interpolation algorithm to obtain an initial imaging result; constructing a spatial model of residual phase error in the spatial domain using a novel time-frequency mapping method; and using the residual phase error spatial model to perform phase error compensation on the effective imaging region block in the initial imaging result to obtain a final SAR image.

[0011] This invention introduces a radius-angle interpolation algorithm into airborne curved trajectory large forward-looking SAR imaging. Under large forward-looking conditions, this algorithm suffers from significant approximations during derivation, resulting in substantial residual phase errors for scene edge targets. This leads to poor image quality at image edges and severely limits the algorithm's focusing depth. This invention addresses this by constructing a spatial model of the residual phase error and designing a residual error compensation method. This improves the algorithm's focusing accuracy under large forward-looking conditions on curved trajectories, expands the effective imaging scene, and significantly mitigates the limitations imposed by residual errors on imaging large forward-looking and large-scene conditions.

[0012] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating a method for compensating for residual spatial variation error in RAA (Rapid Alternating Aspect) of SAR (Rapid Trajectory SAR) provided by the present invention.

[0014] Figure 2 This is a schematic diagram of the imaging scene design provided by the present invention;

[0015] Figure 3 This is an image rendering of existing technology;

[0016] Figure 4 This is an imaging effect diagram of the present invention;

[0017] Figures 5-6 This is a diagram illustrating the effect of imaging measured data according to the present invention. Detailed Implementation

[0018] 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.

[0019] refer to Figure 1 This invention provides a method for compensating for residual spatial variation error in RAA (Rapid Alternating Aspect) of curved trajectory SAR, comprising:

[0020] S100: Acquire high-resolution SAR echo data with large forward-looking angle, and use a radius-angle interpolation algorithm to perform imaging processing on the SAR echo data to obtain the initial imaging result.

[0021] The imaging results in S100 are represented as follows:

[0022] s1(K p ,K θ ) = exp(K p (|r p |-|r c |)+K θ (cosθ p -cosθ c )+e r (1);

[0023] Among them, K p K is the radius wavenumber. θ r is the angular wavenumber. p The slant distance r at the zero point of any azimuth. c Let θ be the slant distance difference at the zero azimuth of the reference point. p Let θ be the angle at time zero of any point's orientation. c The angle at zero moment is the reference point's azimuth.

[0024] S200, using the RAA algorithm to construct a spatial model of the residual phase error;

[0025] S200 of the present invention includes:

[0026] S210, the remaining phase error in the algorithm approximation process is obtained by Taylor series expansion in the azimuth time domain;

[0027] The residual phase error in S210 is expressed as:

[0028]

[0029] Among them, f rf is the distance frequency. c Where c is the carrier frequency, and r is the speed of light. A (η) is the slant distance history at any point, |r c (η)| represents the slant distance history of the reference point, where η is the azimuth time. For the Taylor series expansion of the slant distance history, Δr=|r p |-|r c | represents the difference in slant distance between any point and the reference point at azimuth zero. For the Taylor series expansion of the slant distance history, Δθ = cosθ p -cosθ c Let be the cosine difference of the angle between any point and the reference point at the zero position.

[0030] S220 utilizes the residual phase error in S210 to construct a nonlinear mapping relationship between azimuth time and two-dimensional wavenumber;

[0031] The nonlinear mapping relationship in S220 is expressed as follows:

[0032]

[0033] ξ0=ξ1η * +ξ2η *2 +ξ3η *3 +ξ4η *4 …(4);

[0034]

[0035]

[0036]

[0037] Where, η * For the spatial representation of azimuth and time, η * The superscript indicates the power, and n is the order of the partial derivative.

[0038] S230, based on the nonlinear mapping relationship of S220, and using the series inversion method, the spatial azimuth time expression is obtained;

[0039] The orientation time expression in S230 is as follows:

[0040]

[0041]

[0042] Where k1, k2, k3, and k4 are expansion coefficients;

[0043] S240 utilizes the residual phase error in S210, the nonlinear mapping relationship in S220, and the azimuth-time expression in S230 to construct a spatial model of the residual phase error in the spatial domain.

[0044] The spatial model of the residual phase error in S240 is expressed as follows:

[0045]

[0046] Among them, K θ r is the angular wavenumber. A (η) is the slant distance history at any point, |r c (η)| represents the slant distance history of the reference point. For the Taylor series expansion of the slant distance history, Δθ = cosθ p -cosθ c Let Δr be the cosine difference of the angle between any point and the reference point at time zero, and Δr = |r| p |-|r c | represents the slant distance difference between any point and the reference point at azimuth zero, η * This is the expression for azimuth time in the spatial frequency domain.

[0047] S300: Using the residual phase error spatial domain model of S200, phase error compensation is performed on the effective imaging region block in the initial imaging result to obtain the final SAR image.

[0048] S300 of the present invention includes:

[0049] S310, calculate the effective imaging swath of the spatial domain model of the residual phase error in S200;

[0050] The effective imaging swath width in S310 is expressed as follows:

[0051]

[0052] Where, r A (η) is the slant distance history at any point, |r c (η)| represents the slant distance history of the reference point. For the Taylor series expansion of the slant distance history, Δθ = cosθ p -cosθ c Let Δr be the cosine difference of the angle between any point and the reference point at time zero, and Δr = |r| p |-|r c | represents the slant distance difference between any point and the reference point at azimuth zero, f c η is the carrier frequency, and η is the azimuth time.

[0053] S320, the initial imaging effect is compensated using the effective imaging swath and the residual phase error spatial model to obtain the final SAR image.

[0054] S320 of the present invention includes:

[0055] S321, seed pixels and seed pixel update principles are set in the initial imaging image according to the effective imaging width to determine the effective imaging region block;

[0056] S322, perform azimuth frequency domain upsampling on each effective imaging region block to obtain sampling data S1 m×j (t r ,η);

[0057] S323, Sampling data S1 for each effective imaging region block m×j (t r Perform an azimuth-to-Fourier transform on η) to obtain the Fourier transform data S1 for each effective imaging region block. m×j (t r ,K θ ).

[0058] S324: The pixel matrix of the seed pixels is nonlinearly mapped to obtain the spatial coordinates of each seed pixel, and a spatially variable compensation function is constructed based on the spatial coordinates of each seed pixel.

[0059] The space-variable compensation function in S324 is expressed as follows:

[0060]

[0061] S325, Fourier transform data S1 of each effective imaging region block m×j (t r ,K θ The image is multiplied by the corresponding spatially variable compensation function to compensate for the spatially variable phase error of the initial imaging result and obtain imaging compensation data.

[0062] Among them, t r For distance and time, m is the effective imaging region block number in the azimuth direction, and j is the effective imaging region block number in the range direction;

[0063] S326, Perform an inverse azimuth Fourier transform on the imaging compensation data from S325 to obtain the final focused sub-image S2. m×j (t r ,η);

[0064] S327 will finally focus the sub-image S2 m×j (t r The final focused SAR image S2(t) is obtained by stitching images together using the correlation method. r ,η).

[0065] The imaging processing effect of the present invention will be illustrated by simulation experiments below. The simulation parameters are shown in Table 1.

[0066] Table 1 Simulation Parameters

[0067]

[0068] Combination Figures 2 to 6 , Figure 2 Design drawings for imaging scenes, Figure 3 The image shows the imaging effect of the existing method. Figure 4 This is an image showing the imaging effect of the present invention. Figures 5-6 This is a diagram illustrating the effect of imaging measured data according to the present invention. Figure 3 and Figure 4 The vertical axis represents distance, and the horizontal axis represents direction. (Refer to the above...) Figure 3 It can be observed that for scene edge points P02 and P04, the contour maps obtained using existing techniques show severe aliasing and defocusing in the azimuth direction of both the main lobe and side lobes, indicating that existing techniques lack sufficient depth of focus for target imaging. (Refer to...) Figure 4 , Figure 4 The contour maps obtained by this invention show clear separation of the main lobe and side lobes, exhibiting a good "cross" shape, and good edge point imaging quality, indicating that this invention can improve the focusing depth of the algorithm. Figures 5-6 It can be concluded that the actual imaging effect of the present invention is better.

[0069] Under conditions of large forward-looking deviation, the coupling and spatially varying characteristics of SAR echo signals on curved trajectories are extremely complex. We introduced the classic radius-angle interpolation algorithm into airborne curved-trajectory large forward-looking deviation SAR imaging. Simulation results show that the classic radius-angle interpolation algorithm has significant approximations in its derivation under large forward-looking deviation conditions, resulting in large residual phase errors for scene edge targets. This leads to poor image quality at image edges and significantly limits the algorithm's focusing depth. Therefore, this invention proposes a novel residual error modeling method and designs a residual error compensation method. This improves the focusing accuracy of the algorithm under conditions of large forward-looking deviation on curved trajectories, expands the effective imaging scene, and greatly mitigates the limitations of residual errors on imaging large forward-looking deviation and large scenes.

[0070] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0071] Although this application has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality.

[0072] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for compensating for residual spatial variation error in RAA (Residual Aspect-Adjustable Error) of SAR with curved trajectory, characterized in that, include: S100: Acquire high-resolution SAR echo data with large forward-looking angle, and perform imaging processing on the SAR echo data using a radius-angle interpolation algorithm to obtain the initial imaging result; the imaging result in S100 is expressed as follows: （1）； in, For the radius wavenumber, For angular wavenumber, The slant distance at time zero of any point's azimuth. The difference in slope distance at the zero azimuth of the reference point. Let the angle at zero time be the orientation of any point. The angle at zero moment of reference point azimuth; S200, a new method of time-frequency mapping is used to construct a spatial model of the residual phase error; S300: Using the residual phase error spatial domain model of S200, phase error compensation is performed on the effective imaging region block in the initial imaging result to obtain the final SAR image. S200 includes: S210, the remaining phase error in the algorithm approximation process is obtained by Taylor series expansion in the azimuth time domain; S220 utilizes the residual phase error in S210 to construct a nonlinear mapping relationship between azimuth time and two-dimensional wavenumber; S230, based on the nonlinear mapping relationship of S220, and using the series inversion method, the spatial azimuth-time expression is obtained; S240, using the residual phase error in S210, the nonlinear mapping relationship in S220 and the azimuth-time expression in S230, constructs a spatial model of the residual phase error in the spatial domain. The residual phase error in S210 is expressed as: （2）； in, For distance frequency, For carrier frequency, At the speed of light, Let be the slant distance history of any point. The slant distance history of the reference point For location and time, For the Taylor series expansion of the slant range history, Let S be the difference in slant distance between any point and the reference point at azimuth zero. For the Taylor series expansion of the slant range history, Let be the cosine difference of the angle between any point and the reference point at the zero position. The nonlinear mapping relationship in S220 is expressed as follows: （3）； （4）； （5）； （6）； （7）； in, Spatial representation of location and time. The superscript indicates the power, and n is the order of the partial derivative. The orientation time expression in S230 is as follows: （8）； （9）； in, All are expansion coefficients; The spatial model of the residual phase error in S240 is expressed as follows: （10）。 2. The RAA residual spatial variation error compensation method for curved trajectory SAR according to claim 1, characterized in that, The S300 includes: S310, determine the effective imaging swath width based on the residual phase error spatial domain model in S200; S320, the final SAR image is obtained by compensating the preliminary imaging results using the effective imaging swath and the spatial model of the remaining phase error.

3. The RAA residual spatial variation error compensation method for curved trajectory SAR according to claim 2, characterized in that, The effective imaging swath width in S310 is expressed as: （11）。 4. The RAA residual spatial variation error compensation method for curved trajectory SAR according to claim 3, characterized in that, The S320 includes: S321, seed pixels and seed pixel update principles are set in the initial imaging image according to the effective imaging width to determine the effective imaging region block; S322, perform azimuth frequency domain upsampling on each effective imaging region block to obtain sampling data. ; in, For the distance to the dimensional fast time, This is the effective imaging region block number in the azimuth direction. This refers to the effective imaging region block number in the range direction; S323, sampling data for each effective imaging region block. Perform azimuth-to-Fourier transform to obtain Fourier transform data for each effective imaging region block. ; S324: The pixel matrix of the seed pixels is nonlinearly mapped to obtain the spatial coordinates of each seed pixel, and a spatially variable compensation function is constructed based on the spatial coordinates of each seed pixel. S325, converts the Fourier transform data of each effective imaging region block. Multiplying the data by the corresponding spatially variable compensation function, the spatially variable phase error of the initial imaging result is compensated to obtain the imaging compensation data. S326, Perform an inverse azimuth Fourier transform on the imaging compensation data from S325 to obtain the final focused sub-image. ; S327 will finally focus the sub-image The final focused SAR image is obtained by image stitching using correlation methods. .

5. The RAA residual spatial variation error compensation method for curved trajectory SAR according to claim 4, characterized in that, The space-variable compensation function in S324 is expressed as follows: （12）。 6. The RAA residual spatial variation error compensation method for curved trajectory SAR according to claim 4, characterized in that, S321 includes: S3211, Calculate the pixel density of the initial imaging image based on the effective imaging width; S3212, determine the number of pixels in the effective imaging region block based on the size of the effective imaging region; S3213, by setting the center pixel as the starting seed pixel, using the number of pixels in the effective region block as the growth threshold to control the expansion and merging of the region, gradually merging adjacent pixels into the same region, and when the edge point is reached, using the edge midpoint pixel as the new seed pixel to update the region block as a criterion to form continuous region blocks. S3214, defines a continuous region block as a valid imaging region block.

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