Space-time adaptive processing based on Schatten p-norm minimization for airborne radar

In this paper, we consider the non-stationary clutter suppression for the airborne radar system under the Space-time adaptive processing (STAP) framework. In order to solve the off-grid problem caused by the discretization of angle-Doppler plane in sparse recovery based STAP (SR-STAP) methods and the performance degradation caused by the convex optimization of clutter rank function in atomic norm minimization based STAP (ANM-STAP) methods, we propose a novel STAP method based on Schatten p-norm minimization, termed as SpNM-STAP. In the proposed method, the Schatten p-norm, which can better induce low-rank, is utilized to construct the low-rank model for clutter covariance matrix (CCM). And we derive an efficient optimization algorithm for this model using the alternating direction method of multipliers (ADMM). This method is applicable to both single training sample model and multiple training samples model. Simulation results show that compared with the statistical STAP, SR-STAP and ANM-STAP methods, the proposed algorithm achieves more accurate CCM estimation and has better clutter suppression performance in the scenarios of both side-looking array and non-side-looking array.