A new CFAR detector with automatic censoring

In this paper a new CFAR detector (MOSTM) based on order statistics (OS) and trimmed mean (TM) is introduced. It takes the mean value of OS and TM local estimations as a noise power estimation to set an adaptive threshold, and it also uses the automatic censoring technique proposed by [3]. Under Swerling II assumption, the analytic expressions of P-fa, P-d and ADT are derived for the detector. By comparison with other schemes, the results show that the detection performance of MOSTM is improved both in homogeneous background and in nonhomogeneous environment caused by strong interfering targets and clutter edges, while the sample sorting time of MOSTM is less than half of that of OS.