Intelligent CFAR processor based on data variability

Abstract

An intelligent constant false alarm rate (CFAR) processor to perform adaptive threshold target detection is presented. It employs a composite approach based on the well-known cell averaging CFAR (CA-CFAR), smallest of CFAR (SO-CFAR), and greatest of CFAR (GO-CFAR) processors. Data in the reference window is used to compute a second-order statistic called the variability index (VI) and the ratio of the means of the leading and lagging windows. Based on these statistics, the VI-CFAR dynamically tailors the background estimation algorithm. The VI-CFAR processor provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments including multiple targets and extended clutter edges.