Persymmetric Subspace Detection in Structured Interference and Non-Homogeneous Disturbance

Abstract

This letter addresses the problem of distributed target detection in structured interference and non-homogeneous disturbance. The target signal and interference locate in two linearly independent subspaces with unknown coordinates, while the disturbance is partially homogeneous with an unknown covariance matrix. By incorporating the persymmetric structure of received data, we propose a persymmetric subspace detector for a distributed target, which includes the detector for a point-like target as a special case. Remarkably, the proposed detector is shown to ensure a constant false alarm rate property with respect to both the covariance matrix structure and the power scaling factor. In addition, analytical expressions for the probabilities of false alarm and detection of the special case of the proposed detector for point-like targets are derived, which are verified by Monte Carlo trials. Numerical examples illustrate that the proposed detector can achieve better detection performance, as well as anti-interference ability in training-limited situations.