Adaptive Target Detection in Gaussian Clutter Edges.

This paper addresses the adaptive target detection problem in Gaussian clutter edges. We employ a two-step generalized likelihood ratio test (GLRT) for the detection problem. Our main contribution lies in devising an iterative algorithm in the second step of the GLRT to obtain the maximum likelihood estimation of the unknown clutter-plus-noise covariance matrix, with all available secondary samples in the clutter edge (including the homogeneous and heterogeneous ones). We prove that the proposed iterative algorithm has guaranteed convergence of the objective value. At the analysis stage, we evaluate the performance of the proposed detector and show the superiority over its counterparts. [ABSTRACT FROM AUTHOR]