Adaptive detection of sparsely distributed target in non-Gaussian clutter

Abstract

Adaptive detection of a sparsely distributed target is addressed without secondary data, in non-Gaussian clutter modelled as a spherically invariant random vector. By utilising different estimators of covariance matrix, an adaptive detection scheme is proposed for a sparsely distributed target, based on the generalised likelihood ratio test and the order statistics. Moreover, the adaptive detector with recursive estimator holds approximate constant false alarm rate property with respect to the clutter covariance matrix structure and the statistics of the texture. The performance assessment conducted by Monte-Carlo simulation confirms the effectiveness of the proposed detectors.