Persymmetric adaptive detection in the presence of subspace interference and clutter

Peiqin Tang, Jing Zhang, Can Huang, Hong Xu, Weijian Liu, Jun Liu

Abstract: This paper addresses the persymmetric adaptive detection problem of point-like targets in subspace interference and Gaussian clutter. The targets and interference are modeled as subspace random signals that lie in different deterministic subspaces, but with unknown coordinates. By exploiting the persymmetry property of clutter covariance matrix, we introduce two persymmetric detectors according to the Rao and Wald test criteria. Numerical experimental results illustrate that the proposed persymmetric detectors outperform existing methods in some scenarios, especially under conditions of scarce training data. Moreover, these proposed detectors maintain the constant false alarm rate (CFAR) property.