Performance analysis of modified ordered-statistics CFAR processors in nonhomogeneous environments

Abstract

Order-statistics (OS) is a constant false alarm rate (CFAR) technique which is relatively immune to the presence of interfering targets among the reference cells used to determine the average background level. Unfortunately, the large processing time required by this technique limits its practical use. Two modified versions of this processor, namely ordered-statistics greatest-of (OSGO) and ordered-statistics smallest-of (OSSO), are proposed. These versions require less processing time than OS technique. The purpose of this paper is to provide a complete detection analysis for these processors in nonhomogeneous background noise under chi-square target fluctuation model. Analytical results of performance are presented for OS-CFAR procedures in both multiple target environments with one or more interfering targets and in regions of clutter transitions. The OSGO detector has a better performance in homogeneous background and it accommodates interfering targets in the reference window. In addition, it controls the rate of false alarm in the presence of clutter edges.