%0 Journal Article
%T Performance avalysis of generalized smallest option of CFAR algorithm<br>广义最小选择恒虚警算法的性能分析
%A Meng Xiangwei
%A Guan Jian
%A He You
%A <br>孟祥伟
%A 关键
%A 何友
%J 电子与信息学报
%D 2003
%I 
%X 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.
%K Radar
%K Detection
%K CFAR
%K Order statistics<br>最小选择
%K 雷达
%K 恒虚警率
%K 有序统计
%K CFAR
%K 检测器
%K 加权系统
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=F0AA10B29D336488&yid=D43C4A19B2EE3C0A&vid=C5154311167311FE&iid=CA4FD0336C81A37A&sid=BCA2697F357F2001&eid=EA389574707BDED3&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=2&reference_num=7