ISAR Imaging for Micro-Motion Target Based on FGSR Low-Rank Representation

Stable and efficient inverse synthetic aperture radar (ISAR) imaging methods for micro-motion targets remain a challenging problem. The approximation accuracy of traditional low-rank constraint solving methods based on nuclear norm is required to be improved in high-resolution ISAR imaging and it cannot be applied to large matrices due to the need for singular value decomposition (SVD). In this letter, we replace the nuclear norm constraint with the factor group-sparse regularization (FGSR) without SVD and propose a micro-motion target ISAR imaging method based on FGSR low-rank representation, which improves the imaging accuracy and has a computational efficiency gain of about 5%. Experiments on simulated and measured data verify the effectiveness of the proposed method.