A novel gridless space-time adaptive processing method based cyclic minimization

Abstract

Space-time adaptive processing (STAP) algorithm based on sparse recovery (SR) can effectively suppress clutter in limited sample scenarios. However, the clutter suppression performance will be reduced because of grid mismatch (off-grid), which happens when the clutter ridge fails to match the dictionary. To address this issue, a novel gridless STAP algorithm based on cyclic minimization is proposed in this paper. Compared with the STAP algorithm based on atomic norm minimization (ANM), the proposed algorithm avoids the approximate loss caused by relaxing the rank norm to the nuclear norm. In addition, in order to improve the computational efficiency, we derive a fast iterative structure based on the alternating direction method of multipliers (ADMM) framework. The proposed algorithm offers better clutter suppression performance and excellent computational efficiency, according to a vast collection of experiments based on simulation data and measured data.