Video-SAR Imaging of Dynamic Scenes Using Low-Rank and Sparse Decomposition

Abstract

With the goal of persistent surveillance over a scene of interest, this paper proposes an approach for joint imaging and trajectory extraction of multiple moving objects, using circular video-SAR (ViSAR) mode. Unlike stationary SAR imaging where the phase perturbation of the received signal arises from the platform motion, the moving targets impose additional phase errors, contributing to defocusing of the moving targets’ images. Here, for proper imaging of the moving targets, specifically in low-signal-to-clutter-ratio (SCR) scenarios, their signatures are decomposed from the stationary background clutter by low rank and sparse decomposition (LRSD), before refocusing to their original positions. However, the rotation of the scene, which emerges as a consequence of circular SAR geometry, leads to undesirable decomposition results via LRSD. To facilitate the applicability of LRSD for moving target extraction in our setting, considering this rotation in our model, the decomposition is accomplished while this undesirable effect is automatically compensated for. In addition, to further guarantee that LRSD yields satisfactory decomposition results, phase errors due to platform motion are also jointly corrected along with the clutter separation process. Then, having decomposed the sequential sparse images of moving targets’ signatures, the additional phase errors caused by moving targets are compensated to refocus them to their original positions. After that, these focused sparse images of moving targets are combined to construct a single image where the trajectories of moving targets can be observed. Using this image, a composite image is also constructed, including both the moving objects’ trajectories and the stationary background, which can be used for target tracking applications. Through extensive experimental results we show the effectiveness of the proposed method on both synthetic and real SAR images.