Knowledge-Aided Adaptive Detection in Partially Homogeneous Clutter: Joint Exploitation of Persymmetry and Symmetric Spectrum

In this paper, we address the problem of detecting a point-like target embedded in clutter characterized by a symmetrically structured power spectral density and persymmetric covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the cell under test and the training samples share the same covariance matrix up to an unknown power scaling factor. At the design stage, we jointly exploit the spectral properties of the clutter and the persymmetric structure of the clutter covariance matrix to reformulate the decision problem in terms of real variables with an increased number of training samples. Then, we derive two adaptive detectors relying on the Rao test and a suitable modification of the generalized likelihood ratio test (GLRT). The performance analysis, conducted on both simulated and real radar data, confirms the superiority of the newly proposed receivers over the traditional state-of-the-art counterparts which ignore either the persymmetry or the symmetric spectrum.