ANM-Based Low-Complexity STAP Method for Off-Grid Clutter Spectrum Estimation

Space-time adaptive processing(STAP) based on sparse recovery(SR-STAP) is a high-efficiency method for clutter suppression, which can obtain excellent suppression performance with a small number of snapshots. However, using the discrete dictionary in SR-STAP will cause the off-grid problem, that is, the dictionary atoms do not match the clutter ridge, which will degrade the clutter suppression performance. In this paper, a low-complexity STAP method based on atomic norm minimization(ANM) method is proposed to overcome this issue. In the proposed method, the ANM method is used to avoid off-grid problem, and the dimension of the constraint matrix is reduced under the inspiration of the decoupling ANM(DANM) method. Then, a fast iterative algorithm based on augmented lagrange multiplier(ALM) method is derived to further reduce the computational complexity. Finally, the simulation proves the effectiveness of proposed method.