Performance analysis of a new greatest of selection CFAR detector

Abstract

In this paper, a new greatest of selection CFAR detector (OSTMGO) based on order statistics (OS) and trimmed mean (TM) is proposed. It takes the greatest value of OS and TM local estimations as a global noise power estimation, and it also uses the automatic censoring technique proposed by He You (1994). Under the Swerling II assumption, the analytic expressions of P/sub fa/, P/sub d/ and ADT of OSTMGO are derived. By comparison with other schemes, the results show that the detection performance of OSTMGO is good both in homogeneous background and in nonhomogeneous environments caused by strong interfering targets and clutter edges; particularly in the clutter edges situation, the peak of false alarm rate of OSTMGO decreases an order of magnitude more than that of GOSGO, while the sample sorting time of OSTMGO is less than half of that of OS.