Imaging Algorithm for Circular SAR Based on Geometric Constraints

Circular synthetic aperture radar (CSAR) has the advantage of providing a comprehensive view of the scene. Back-projection (BP) algorithm and Fast factorized BP (FFBP) algorithm can be used for image reconstruction under imaging conditions with bandwidths and accumulation angles of any trajectory or size and is independent of the azimuth-invariant assumption, which are more suitable for CSAR imaging. However, the BP algorithm has a large computational amount, and the FFBP algorithm corresponds to the update of the image coordinate system, which increases the difficulty of image accumulation. To improve the speed and quality of CSAR imaging, this paper proposes a CSAR sub-aperture imaging method based on the geometry constraints. Firstly, the principle of coherence superposition of CSAR sub-aperture in the local quasi-polar coordinate system (LQPCS) is derived; secondly, the flow of imaging algorithm for CSAR based on geometric constraints is given; thirdly, The simulated imaging results verify the correctness of the theoretical analysis and the effectiveness of the proposed method.