A NEW GREATEST OF SELECTION CFAR DETECTOR BASED ON TRIMMED MEAN
一种新的基于剔除平均的最大选择恒虚警检测器

A new greatest of selection CFAR detector (TMGO) based on trimmed mean (TM) is proposed in this paper. It takes the greatest value of two local estimations created by leading and lagging reference window which apply TM method as a noise power estimation, and it also uses the automatic censoring technique proposed by He You (1994). It is shown that the detection performance of TMGO is superior to that of GOSGO or OSGO in both homogeneous background and nonhomogeneous environment caused by strong interfering targets and clutter edges, while the sample sorting time of TMGO is less than a half of that of OS. Some current CFAR algorithms such as GO,GOSGO or OSGO, CMGO becomes the special cases of TMGO.