Using persymmetric property in knowledge-aided space-time adaptive processing

In space-time adaptive processing (STAP), if incorporating a priori knowledge, the covariance matrix estimation and detection performance can be substantially improved with the heterogeneous environment effects being reduced. In addition, besides the employed priori information, the commonly exhibiting persymmetric structure in radar systems with symmetrically spaced linear array and pulse train can also be used to improve the STAP performance. In this paper, by exploiting the structure property of the covariance matrix, we propose a new knowledge-aided method which requires fewer samples and computes fully adaptive such that we can obtain the minimum mean square error estimate of the interference-plus-noise covariance matrix. At last, numerical simulations illustrate the effectiveness of the newly proposed method.