Knowledge-Aided Group GLRT for Range Distributed Target Detection in Partially Homogeneous Environment

Abstract

In this paper, we consider the range distributed target detection in partially homogeneous clutter which satisfies a different statistical property in adjacent range cells. The group method wherein adjacent cells with slightly varied statistics are in the same group is presented firstly, which can improve the accuracy of modeling clutter. We assume that all texture of the compound Gaussian clutter satisfies an inverse Gamma distribution but scale and shape parameters in those groups differ from one another. The group generalized likelihood ratio test (G-GLRT) developed here concerns the cells group effects on deducing the GLRT. Considering a knowledge-aided (KA) model that tracking into account the partially homogeneous training samples, we develop a KA-G-GLRT for range-spread target detection and verify the constant false alarm rate (CFAR) with respect to the estimated covariance matrix of speckle. Experimental results are presented to illustrate the performance and effectiveness of the KA-G-GLRT in real clutter data.