Efficient SAR real-time imaging under generalized orthogonal polar coordinate format algorithm

Abstract

The invention relates to a synthetic aperture radar (SAR) imaging technology, in particular to a novel algorithm which is a generalized orthogonal polar coordinate format algorithm (GOPFA) and is used for optimizing a polar coordinate format algorithm (PFA) in a video synthetic aperture radar (SAR). The objective of the invention is to solve the problems of calculation efficiency and imaging precision of a traditional PFA under a large-range scene, a high frame rate and a complex terrain. Computing efficiency is improved by adopting generalized resampling and wavefront curvature compensation methods, a digital elevation model (DEM) is introduced for orthogonal correction, terrain distortion is eliminated, and finally efficient and accurate video SAR imaging is achieved. The algorithm is suitable for large-range video SAR imaging, is widely applied to the fields of environment monitoring, disaster assessment, military surveillance and the like, and can provide real-time and high-precision geographic reference images.

Description

Technical Field

[0001] The present invention relates to synthetic aperture radar (SAR) imaging technology, especially the optimization of the polar format algorithm (PFA) in video synthetic aperture radar (Video SAR). Specifically, the present invention proposes a Generalized Orthorectified Polar Format Algorithm (GOPFA) to improve the imaging efficiency and accuracy of Video SAR in large-scale scenes and complex terrains, and is particularly suitable for real-time and high-resolution SAR imaging applications. Background Art

[0002] Video SAR technology has the advantages of high resolution, continuous imaging, and working under any weather conditions, and has important applications in the fields of military surveillance, environmental monitoring, disaster assessment, etc. However, although the traditional polar format algorithm (PFA) has high computational efficiency, it also has some limitations: 1. Efficiency issue: When dealing with large-scale scenes and high-frame-rate Video SAR, the traditional PFA algorithm often faces high computational load and low efficiency. 2. Imaging scene limitation: The effective imaging range of the traditional PFA for large scenes is limited. Especially under complex terrain conditions, the wavefront curvature error and defocusing problem significantly affect the imaging quality. 3. Terrain influence: On non-flat terrains, PFA imaging may produce large errors, resulting in image distortion and unable to provide accurate georeference information.

[0003] To overcome the above problems, the Generalized Orthorectified Polar Format Algorithm (GOPFA) is proposed, which can effectively improve the computational efficiency and expand the imaging range. At the same time, by introducing the digital elevation model (DEM), the terrain distortion is effectively compensated, and accurate georeference imaging is achieved. This algorithm can eliminate the distortion caused by the terrain while maintaining high efficiency, and further improve the overall performance of Video SAR imaging. Summary of the Invention

[0004] The purpose of the present invention is to propose a new type of Video SAR imaging algorithm, the Generalized Orthorectified Polar Format Algorithm (GOPFA), aiming to overcome the limitations of the traditional PFA in large scenes, high frame rates, and complex terrains.

[0005] With this new algorithm, the problems of wavefront curvature error and terrain distortion can be effectively solved while maintaining high computational efficiency. The specific implementation steps are as follows: S1: Generalized resampling and wavefront curvature compensation: First, use the generalized resampling method to efficiently process SAR data, compensate for the wavefront curvature error, ensure the efficiency of the processing process, and expand the effective imaging range of PFA. S2: Orthogonal correction and terrain compensation: After the generalized resampling process, introduce the orthogonal correction technology, and correct the distortion and defocus caused by the terrain through the digital elevation model (DEM) to ensure that the finally generated image is a high-precision georeferenced image. S3: Non-uniform Fourier transform (NuFFT-3): Finally, use the non-uniform fast Fourier transform (NuFFT-3) to perform Fourier transform on the orthogonally corrected image to quickly generate a high-quality SAR image.

[0006] With this algorithm, not only can the computational efficiency of video SAR imaging be improved, but also the distortion can be eliminated and the geographical accuracy of the image can be improved under complex terrains. This algorithm is applicable to real-time video SAR imaging of large-scale scenes and dynamic trajectories, and is widely used in fields such as environmental monitoring, disaster assessment, and military surveillance. Brief Description of the Drawings

[0007] Figure 1 It is a flowchart of efficient SAR real-time imaging based on the generalized orthogonal polar coordinate format algorithm. Figure 2 It is a comparison of the effects of Google optical images and SAR images Detailed Implementation Manner

[0008] 1. Generalized resampling and wavefront curvature compensation

[0009] First, adopt the generalized resampling method to optimize the frequency-domain resampling of video SAR data to improve the computational efficiency. Specifically, the formula for calculating the distance difference of the target is as follows:

[0010] S i =exp(-jKΔR i )=exp[-jK(ΔR 0 +Δi)]·S 0 ·exp(-jKΔi)

[0011] where S 0 is the initial signal, K is the wave number, ΔR 0 is the reference distance, Δi is the local distance difference, and ΔR i is the total distance difference.

[0012] At the same time, compensate for the wavefront curvature error using the following formula:

[0013]

[0014] Among them, P(r) is the image to be processed, k x and k y are frequency domain coordinates, f is the focal length, is the compensated image.

[0015] 2. Orthogonal Correction and Topographic Compensation

[0016] Based on the processed data of generalized resampling, an orthogonal correction method is adopted, and a digital elevation model (DEM) is used for topographic correction. Through the following formula, the orthogonal correction position of the image is calculated:

[0017]

[0018] Among them, x i and y i respectively represent the image coordinates after orthogonal correction, x c , y c is the center coordinate of the image, X i , Y i is the geographic coordinate of the target, E i , F i , A i are the mapping coefficients calculated through the digital elevation model (DEM).

[0019] This process can effectively eliminate the influence of terrain undulation on the image. Especially in non-flat terrains, through the compensation and correction of DEM, the image is closer to the real geographical location.

[0020] 3. Non-uniform Fourier Transform (NuFFT-3)

[0021] Finally, the non-uniform fast Fourier transform (NuFFT-3) is used to perform Fourier transform on the orthogonally corrected image. NuFFT-3 can efficiently process non-uniformly distributed wavenumber data, avoiding the high computational complexity in traditional Fourier transform.

[0022] Specifically, given the orthogonally corrected image data, its Fourier transform is calculated through the NuFFT-3 formula:

[0023]

[0024] Among them, I(x r , y r ) is the final orthogonally corrected image, is the signal after generalized resampling and wavefront curvature compensation, is the wavenumber domain coordinate, x r, y r is the pixel position after reconstruction.

[0025] Using NuFFT-3 for Fourier transform can significantly improve the speed of image reconstruction and reduce the computation time. Especially in large-scale scenarios, this acceleration effect is more obvious.

Claims

1. Application of a generalized orthorectified polar format algorithm (GOPFA) in synthetic aperture radar (SAR) imaging. S1: The generalized resampling method is used to process SAR data, compensate for the wavefront curvature error, and expand the effective imaging range of the polar format algorithm (PFA). S2: Orthogonal correction technology is used in combination with the digital elevation model (DEM) to correct the distortion and defocus caused by terrain and generate high-precision georeferenced images. S3: Use non-uniform fast Fourier transform (NuFFT-3) to perform Fourier transform on the orthogonally corrected image to quickly generate high-quality SAR images.

2. The application of the generalized orthogonal polar coordinate format algorithm in SAR imaging according to claim 1, characterized in that: The generalized resampling method includes: optimizing the frequency domain resampling of video SAR data to improve the calculation efficiency; and processing the target by using the distance difference calculation formula: S4: Specifically, the target distance difference calculation formula is as follows: Si=exp(-jKΔR i )=exp[-jK(ΔR0+Δi)]·S0·exp(-jKΔi) S5: where S0 is the initial signal, K is the wave number, ΔR0 is the reference distance, Δi is the local distance difference, ΔR i is the total distance difference.

3. The application of the generalized orthogonal polar coordinate format algorithm in SAR imaging according to claim 1, characterized in that: Compensate for the wavefront curvature error: S6: Use the following formula: S7: Where P(r) is the image to be processed, k x and k y is the frequency domain coordinate, f is the focal length, is the compensated image.

4. The application of the generalized orthogonal polar coordinate format algorithm in SAR imaging according to claim 1, characterized in that: The orthogonal correction and terrain compensation includes calculating the mapping coefficients using a digital elevation model (DEM): S8: Calculate the orthogonal correction position of the image by the following formula: S9: where x i and i They represent the image coordinates after orthogonal correction, x c ,y c is the center coordinate of the image, X i , Y i is the geographic coordinates of the target, E i , F i , A i It is the mapping coefficient calculated by the digital elevation model (DEM).

5. The application of the generalized orthogonal polar coordinate format algorithm in SAR imaging according to claim 1, characterized in that: Using the Non-Uniform Fast Fourier Transform (NuFFT-3): S10: Specifically, the calculation formula is: S11: where I(x r ,y r ) is the final orthogonal correction image, is the signal after generalized resampling and wavefront curvature compensation, is the wavenumber domain coordinate, x r ,y r is the reconstructed pixel position.

6. A SAR imaging system based on the generalized orthogonal polar coordinate format algorithm of claim 1, characterized in that: The system includes a data processing module for performing generalized resampling and wavefront curvature compensation; a terrain correction module for performing orthogonal correction in combination with a digital elevation model (DEM); and an image reconstruction module for performing non-uniform fast Fourier transform (NuFFT-3) and generating high-quality SAR images.

7. The SAR imaging system according to claim 6 is suitable for real-time video SAR imaging in large-scale scenes and complex terrains, and can provide high-resolution, low-distortion and high-precision georeferenced images.

8. The SAR imaging system according to claim 6, characterized in that: The system is suitable for environmental monitoring, disaster assessment, military surveillance and other fields.

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