Performance analysis of the generalized order statistics CFAR detector

Abstract

The main objective of this dissertation work is to develop an unified CFAR structure and to derive uniformed formulas for detection and false alarm probabilities in closed form. By this approach, one can obtain performance of various CFAR detectors, particularly whose performance has not been analyzed yet. There is a lot of CFAR detector for single pulse detection using the sliding window techniques. These CFAR detectors have their own fixed structure, respectively, i.e., fixed estimate of the background clutter-plus-noise level. A generalized CFAR model, termed the GOS CFAR detector which is a generalized structure of the OS CFAR processors, is proposed. Unified formulas for the false alarm and detection probabilities in homogeneous as well as nonhomogeneous situations are derived in closed form. By properly choosing the coefficients of the GOS filter, one can realize the OS CFAR processor, the TM CFAR processor, and CMLD as well as the CA CFAR processor as special cases. By this model, one can implement the $2^N-1$ kinds of CFAR processor by the combination of coefficients of the GOS filter. In order to get rid of the capturing effect in multiple target situations, we also propose ACMLD, which is obtained by applying the GOS CFAR detector to this environments. ACMLD estimates adaptively the number of interferers in a reference window in short time interval compared to GCMLD when there exist a few interfering targets. This scheme can also be applied to the OS CFAR processor to determine the number of interferers and the detection threshold. Using the unified CFAR structure and the performance formulas, we compare the detection performance of ACMLD with those of other CFAR processors in multiple target situations. And then we compare the false alarm rates of various CFAR processors at clutter edge. The analytical results show that ACMLD reveals little CFAR loss compared with the CA CFAR processor in homogeneous situation. In multiple target situations, ACML...