Rapidly Adaptive CFAR Detection by Merging Individual Decisions from Two-Stage Adaptive Detectors

Abstract

This paper addresses the problem of target detection in adaptive arrays in situations where only a small number of training samples is available. Within the framework of two-stage adaptive detection paradigm, we propose a new two-stage (TS) joint loaded persymmetric- Toeplitz adaptive matched filter (JLPT -AMF) detector. This new detector combines, using a joint detection strategy, individual scalar CFAR decisions from two rapidly adaptive detectors: a TS TAMF detector and a TS LPAMF detector. The former is based on a TMI filter, which is an adaptive array filter employing a Toeplitz covariance matrix estimator introduced in [1]. The latter is based on an adaptive LPMI filter that uses diagonally loaded persymmetric covariance matrix (CM) estimate inversion. The TS JLPT -AMF detector ensures the constant false alarm rate (CFAR) property independently of the antenna array dimension M, the interference CM, and the number of training samples NCME to be used for estimating this CM. This new detector exhibits highly reliable detection performance, which is robust to the angular separation between the sources, even when NCME is about m/2 ~ m, m is the number of interference sources. The robustness of the proposed adaptive detector to the angular separation is analytically proven and verified with statistical simulation.