Knowledge-aided sparse recovery STAP algorithm with off-grid self-calibration for airborne radar

Abstract

Space-time adaptive processing (STAP) for airborne radar may cause lattice mismatch during sparse recovery processing, which is the off-grid problem. The off-grid problem may lead to degradation of STAP performance. To cope with this problem, this paper proposes a knowledge-aided sparse recovery STAP algorithm with off-grid self-calibration (AO-SR-STAP). The snapshots are decomposed by sparse processing firstly. The off-grid of spare dictionary is calibrated and dense interferences are removed according to the knowledge of clutter distribution. A standard steering vector set is constructed by the prior knowledge of clutter distribution, which is used to calibrate the off-grid of sparse dictionary. The dense interferences in snapshots are removed with knowledge of clutter distribution. Hence, the clutter information of snapshots is estimated accurately and the target in cell under test can be detected completely. The advantage of this algorithm is that the off-grid of sparse dictionary can be calibrated, and dense interferences are filtered effectively. Simulation experiments verify the effectiveness and robustness of the proposed algorithm.