Exploiting persymmetry for CFAR detection in compound-Gaussian clutter

This paper deals with the problem of estimating the covariance structure of a compound-Gaussian process and of its application to adaptive radar detection in clutter-dominated disturbance. The proposed estimator exploits the persymmetry property typical of Toeplitz covariance matrices and is based on secondary data, free of signal components, and with the same covariance structure of the cell under test. We prove that, plugging the proposed covariance estimator into the normalized matched filter (NMF) [E. Conte, M. Lops, and G. Ricci, 1995], leads to an adaptive detector which, irrespective of the shape of the clutter power spectral density (psd), ensures the constant false alarm rate (CFAR) property with respect to both the clutter covariance matrix as well as the statistics of the texture. Finally, we show that this adaptive receiver has an acceptable loss with respect to its non-adaptive counterpart and outperforms the previously proposed CFAR adaptive NMF (ANMF) [E. Conte, A de Maio, and G. Ricci, 2002].