Abstract
To implement fast reconstruction of the multiple measurement vectors (MMV) problem under arbitrary sparse structures, a fast complex linearized Bregman iteration (FCLBI) algorithm is proposed and applied to inverse synthetic aperture radar (ISAR) imaging. First, a signal model of an arbitrary sparse structure is established and its signature is analyzed. Second, an iterative formula for the FCLBI algorithm is deduced in a complex domain to recover the signals of the arbitrary sparse structure, thus extending its universality for complex-valued data. Third, by combining stagnation step estimation and sensing matrix optimization, the total iterative numbers are decreased to improve computational efficiency. Finally, the algorithm is applied to ISAR imaging, which reduces imaging time. Simulations and experiments with real-world data show the effectiveness and robustness of the proposed algorithm.
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