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Integrating Motion Compensation With Polar Format Interpolation for Enhanced Highly Squinted Airborne SAR Imagery
Author(s) -
Fengfei Wang,
Lei Zhang,
Yunhe Cao,
Guanyong Wang
Publication year - 2019
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2019.2946190
Subject(s) - computer science , motion compensation , synthetic aperture radar , computer vision , artificial intelligence , azimuth , interpolation (computer graphics) , image resolution , kernel (algebra) , motion estimation , radar imaging , inverse synthetic aperture radar , side looking airborne radar , residual , algorithm , motion (physics) , radar , bistatic radar , optics , mathematics , telecommunications , combinatorics , physics
Spatial-variant motion compensation (MOCO) is critical for high resolution and highly squinted airborne synthetic aperture radar (SAR) imaging. Conventional imaging strategies generally perform systematic imaging algorithm and motion compensation algorithm in a separate way, the residual azimuth-variant motion error usually causes defocusing in the image. It is difficult for existing post-filtering strategies to realize high precision and high efficiency imaging simultaneously. To solve this problem, a novel parametric polar format algorithm (PPFA) is proposed in this paper. The polar format interpolation kernel is redefined and improved by inducing motion error parameter, so the proposed algorithm can realize fast imaging and precise spatial-variant motion compensation at the same time. The proposed algorithm has advantage of high processing efficiency as polar format algorithm (PFA) and effectively improves the focusing precision. Extensive comparisons with conventional algorithms illustrate the superiority of the proposed algorithm in both precision and efficiency.

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