An Improving EFA for Clutter Suppression by Using the Persymmetric Covariance Matrix Estimation

The extended factored approach (EFA) is believed to be one of the most efficient and practical space–time adaptive processing (STAP) algorithms for clutter suppression in an airborne radar system. However, it cannot effectively work in the airborne radar system with large antenna array for the huge computational cost and the lack of training sample. To solve these problems, a bi-iterative algorithm based on the persymmetric covariance matrix estimation is proposed in this paper. Firstly, the clutter covariance matrix is estimated by using the original data, the constructed spatial transformed data, the constructed temporal transformed data and the constructed spatial–temporal transformed data. Secondly, the spatial weight vector in EFA is decomposed as the Kronecker products of two short weight vectors. Finally, the bi-iterative algorithm is exploited to obtain the desired weight vectors. Thus, the improving EFA with small training sample demanding is realized. Experimental results demonstrate the effectiveness of the proposed method under small training sample support.