Performance avalysis of generalized smallest option of CFAR algorithm

In order to enhance the performance of OSSO, the Generalized Smallest Op-tion(GSO) of logic CFAR algorithm is proposed in this paper. For this CFAR. algorithms, it splits the reference window into two sub-windows and uses the linear combined order statis-tics to create two local noise power estimations, the smallest of them is used to set an adaptive threshold. How to select the weighted coefficient of the linear combined order statistics in the practical situation, several suggestions are given. In the special cases of GSO, QBWSO, TMSO, CMSO, OSSO and SO methods are deduced. The analytic results show that the detection per-formance of QBWSO and TMSO is superior to that of OSSO both in homogeneous background and in multiple target situation, the CFAR loss of QBWSO is slightly lower than that of TMSO in homogeneous background. In homogeneous background, the detection performance of SO is the best.