LAUSR

In this letter, we consider the problem of constant false alarm rate (CFAR) detection in heterogeneous scenarios. We assume that the covariance matrices of the training samples are different from that of the cell under test and employ prior statistical knowledge to describe the degree of heterogeneity. We derive an approximate expression of the average signal-to-clutter-noise ratio loss of the adaptive detector constructed with the maximum likelihood estimate of the covariance matrix obtained by solving the fixed-point equation. We validate the CFAR property of the detector and derive the asymptotic expression of the probability of false alarm. Exploiting such prior knowledge can significantly reduce the adverse effects of the heterogeneous training samples. Simulations validate the proposed theoretical results.