In this paper, we propose a synthetic aperture radar (SAR) moving-target imaging approach that exploits the low-rank and sparse decomposition (LRSD) of subaperture data. The low-rank component consists of the static background whereas the sparse component captures the moving targets. This allows the reconstruction of a full resolution moving target image separate from the static background image after LRSD. Furthermore, it facilitates the applicability of sparsity-driven moving target imaging in low signal to clutter ratio (SCR) scenarios. We demonstrate the effectiveness of our approach with experiments on synthetic as well as real SAR data.
Mubashar Yasin, Ahmed Shaharyar Khwaja, and Müjdat Çetin, "A subaperture based approach for SAR moving target imaging by low-rank and sparse decomposition," Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 106470K (Presented at SPIE Defense + Security: April 19, 2018; Published: 27 April 2018); https://doi.org/10.1117/12.2304982.
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