Sparsely range-spread target detector and performance assessment

In the background where the clutter is modeled as a spherically invariant random vector, the adaptive detection of sparsely range-spread targets is addressed. By exploiting the order statistics and the covariance matrix estimators, the adaptive detector (AD) is assessed. Herein, the detectors of AD-SCM and AD-NSCM are proposed based on the sample covariance matrix (SCM) and normalized sample covariance matrix (NSCM), respectively. In terms of constant false alarm rate properties and detection performance, the AD-NSCM outperforms the AD-SCM and the existing detector of modified generalized likelihood ratio. Finally, the performance assessment conducted by simulation confirms the effectiveness of the proposed detectors. Copyright © 2013 Acta Automatica Sinica. All rights reserved.