Sparse Bayesian learning using hierarchical synthesis prior for STAP

Abstract

Space–time adaptive processing (STAP) can effectively detect moving targets in the background of ground clutter, but the performance will drop sharply when the training samples are limited. In this paper, to improve the clutter suppression performance when the training samples are limited, the authors propose a novel STAP algorithm based on sparse Bayesian learning (SBL) using a hierarchical synthesis prior. Firstly, we construct a novel three-level hierarchical synthesis prior (HSP) model, which promotes the sparsity more significantly than traditional priors used in SBL. Secondly, in the framework of type-II maximum likelihood approach, a novel iterative update criterion for hyperparameters is derived. Thirdly, in order to reduce the computational burden, the authors design a novel local space–time dictionary to transform the full-dimensional clutter spectrum recovery problem into a local clutter spectrum recovery problem. Numerical results with both simulated and measured data demonstrate the excellent performance and relatively high computational efficiency of the proposed method.