Data-Dependent Clustering-CFAR Detector in Heterogeneous Environment

Abstract

This paper devises a new constant false alarm rate (CFAR) detection scheme to deal with the problem of radar target detection in heterogeneous environment. The proposed scheme, called “clustering-CFAR detector,” is data dependent and composed of three stages: an adaptive clustering procedure that, exploiting the recorded measurements of the clutter environment, divides the detection area into different classes to provide auxiliary information, a dynamic reference cell selector that chooses appropriate secondary data according to the classes, and a conventional CFAR processor to make the final decision about the target presence. The performance of “clustering-CFAR detector” is analyzed by computer simulation and public radar measured data (IPIX data and MSTAR data), and compared with existing CFAR detectors. The results show that the new detector achieves a better performance in the aspects of terrain classification, control of false alarm points, and probability of detection.