广义最小选择恒虚警算法的性能分析

为了改善OSSO或GOSSO方法的性能，该文基于加权线性组合的有序统计量提出了广义最小选择（GSO）恒虚警检测器。文中讨论了线性组合有序统计量加权系数的选择与检测器性能的关系，在GSO特殊加权系数场合，提出了QBWSO,TMSO,CMSO三种性能较为优良的检测器，分析结果表明，TMSO和QBWSO在均匀背景及多目标环境中的性能均比OSSO的性能获得了改善，QBWSO在均匀背景中的性能比TMSO的略强；在均匀背景中，SO的性能最好。

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 statistics 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 performance 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.