An Improving EFA for Clutter Suppression by Using the Persymmetric Covariance Matrix Estimation
作者： Yan Zhou , Lin Wang , Xiaoxuan Chen , Cai Wen , Bo Jiang
DOI: 10.1007/S00034-017-0743-Y
关键词:
摘要: 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 system with large antenna array huge computational cost lack training sample. To solve these problems, a bi-iterative algorithm based on persymmetric covariance matrix estimation proposed this paper. Firstly, estimated by using original data, constructed spatial transformed temporal data spatial–temporal data. Secondly, weight vector EFA decomposed as Kronecker products two short vectors. Finally, exploited obtain desired Thus, improving small sample demanding realized. Experimental results demonstrate effectiveness method under support.
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