Fast STAP Method Based on PAST with Sparse Constraint for Airborne Phased Array Radar

Abstract

Conventional space-time adaptive processing (STAP) is difficult to effectively suppress nonhomogeneous clutter in practical airborne phased array radar systems owing to the requirements of large independent and identically distributed training samples and the high complexity involved in the inversion of a high dimensional matrix. In order to improve the clutter suppression performance with a small training sample supports, a fast STAP method based on projection approximation subspace tracking (PAST) with sparse constraint is proposed in this paper. In the proposed method, based on the low-rank property of the clutter covariance matrix, a sparse constraint is imposed in the cost function of PAST, and the adaptive weight vector is then derived iteratively. Because of the sparse constraint in PAST, the proposed method provides a more robust and stable estimation of the clutter subspace, especially when only a small set of training samples is available. Moreover, the proposed method not only achieves lower computational complexity compared with existing sparsity constrained STAP methods, but also better performance and faster convergence compared with conventional STAP methods without sparse constraint. The effectiveness of the proposed method is verified based on simulated and actual airborne phased array radar data.