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Main Authors: Jiang, Jiawei, Li, Yinwei, Luo, Shaowen, Li, Ping, Zhu, Yiming
Format: Preprint
Published: 2023
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Online Access:https://arxiv.org/abs/2401.10252
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author Jiang, Jiawei
Li, Yinwei
Luo, Shaowen
Li, Ping
Zhu, Yiming
author_facet Jiang, Jiawei
Li, Yinwei
Luo, Shaowen
Li, Ping
Zhu, Yiming
contents Video synthetic aperture radar (SAR) is attracting more attention in recent years due to its abilities of high resolution, high frame rate and advantages in continuous observation. Generally, the polar format algorithm (PFA) is an efficient algorithm for spotlight mode video SAR. However, in the process of PFA, the wavefront curvature error (WCE) limits the imaging scene size and the 2-D interpolation affects the efficiency. To solve the aforementioned problems, a beam-segmenting PFA based on principle of chirp scaling (PCS), called BS-PCS-PFA, is proposed for video SAR imaging, which has the capability of persistent imaging for different carrier frequencies video SAR. Firstly, an improved PCS applicable to video SAR PFA is proposed to replace the 2-D interpolation and the coarse image in the ground output coordinate system (GOCS) is obtained. As for the distortion or defocus existing in the coarse image, a novel sub-block imaging method based on beam-segmenting fast filtering is proposed to segment the image into multiple sub-beam data, whose distortion and defocus can be ignored when the equivalent size of sub-block is smaller than the distortion negligible region. Through processing the sub-beam data and mosaicking the refocused subimages, the full image in GOCS without distortion and defocus is obtained. Moreover, a three-step MoCo method is applied to the algorithm for the adaptability to the actual irregular trajectories. The proposed method can significantly expand the effective scene size of PFA, and the better operational efficiency makes it more suitable for video SAR imaging. The feasibility of the algorithm is verified by the experimental data.
format Preprint
id arxiv_https___arxiv_org_abs_2401_10252
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle A Beam-Segmenting Polar Format Algorithm Based on Double PCS for Video SAR Persistent Imaging
Jiang, Jiawei
Li, Yinwei
Luo, Shaowen
Li, Ping
Zhu, Yiming
Computer Vision and Pattern Recognition
Video synthetic aperture radar (SAR) is attracting more attention in recent years due to its abilities of high resolution, high frame rate and advantages in continuous observation. Generally, the polar format algorithm (PFA) is an efficient algorithm for spotlight mode video SAR. However, in the process of PFA, the wavefront curvature error (WCE) limits the imaging scene size and the 2-D interpolation affects the efficiency. To solve the aforementioned problems, a beam-segmenting PFA based on principle of chirp scaling (PCS), called BS-PCS-PFA, is proposed for video SAR imaging, which has the capability of persistent imaging for different carrier frequencies video SAR. Firstly, an improved PCS applicable to video SAR PFA is proposed to replace the 2-D interpolation and the coarse image in the ground output coordinate system (GOCS) is obtained. As for the distortion or defocus existing in the coarse image, a novel sub-block imaging method based on beam-segmenting fast filtering is proposed to segment the image into multiple sub-beam data, whose distortion and defocus can be ignored when the equivalent size of sub-block is smaller than the distortion negligible region. Through processing the sub-beam data and mosaicking the refocused subimages, the full image in GOCS without distortion and defocus is obtained. Moreover, a three-step MoCo method is applied to the algorithm for the adaptability to the actual irregular trajectories. The proposed method can significantly expand the effective scene size of PFA, and the better operational efficiency makes it more suitable for video SAR imaging. The feasibility of the algorithm is verified by the experimental data.
title A Beam-Segmenting Polar Format Algorithm Based on Double PCS for Video SAR Persistent Imaging
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2401.10252