Adaptive Persymmetric Subspace Detector for Distributed Target

Abstract

In this paper, we deal with the problem of adaptive detection for distributed target in homogeneous Gaussian clutter with unknown persymmetric covariance. The distributed target is located in a multi-rank subspace and its coordinates are unknown. We devise a persymmetric subspace detector by utilizing the one-step Rao criteria. Ultimately, the numerical results show that, the proposed detector is constant false alarm rate (CFAR) with respect to unknown clutter covariance matrix. Moreover, it also has the edge on detection performance by comparing with the existing unstructured detectors, especially in training-limited scenarios.