Performance Analysis of Stap Algorithms Based on Fast Sparse Recovery Techniques

In the field of space-time adaptive processing (STAP), spare recovery type STAP (SR-STAP) algorithms exploit formulation of the clutter estimation problem in terms of sparse representation of a small number of clutter positions among a much larger number of potential positions in the angle-Doppler plane, and provide an effective approach to suppress the clutter especially in very short snapshots. However, it differs from many situations encountered by other SR application fields in the following ways: (i) it does not require to obtain the exact solution; (ii) it highly requires low-complexity approaches. In this paper, we focus on the performance analysis and parameters setting of STAP algorithms based on five representative fast SR techniques, namely, the compressive sampling matching pursuit, the sparse reconstruction by separable approximation, the fast iterative shrinkage-thresholding algorithm, the focal underdetermined system solution and the smoothed l0 norm method.