一种新的基于有序统计和剔除平均的最大选择恒虚警检测器

A New Greatest Selection CFAR Detector Based on Ordered Statistics and Trimmed Mean

本文基于有序统计（OS）和剔除平均（TM）提出了一种新的恒虚警检测器（OSTMGO），它的前尚滑窗和后尚滑窗分别采用OS和TM来产生局部估计，再选择两者之中的最大值作为杂波功率水平的估计，并应用了文献［4］提出的自动筛选技术，在Swerling型目标假设下，本文推导出了它的Pfa、Pd和度量的解析表达式，并与其它方案进行了比较，分析结果表明它在均匀背景及多目标和杂波边缘引起的非均匀背景中，均具有较好的检测性能，尤其是在杂波边缘引起的非均匀背景中，它的虚警尖峰比GOSGO减少了一个数量级，并且它的样本排序时间还不到OS的一半．

A new CFAR detector(OSTMGO)based on ordered statistics(OS)and trimmed mean(TM)is proposed in this paper.It takes the greatest value of OS and TM local estimations as a noise power estimation,and it also uses the automatic censoring technique proposed by[4].Under Swerling Ⅱassumption,the analytic expressions of Pfa, Pd and ADT of OSTMGO are derived.By comparison with other schemes,the results show that the detection performance of OSTMGO is good both in homegeneous background and in nonhomogeneous en vironment caused by strong interfering targets and clutter edges,particularly in clutter edges situation,the spike of false alarm rate of OSTMGO decreases an order of magnitude than that of GOSGO,while the sample sorting time of OSTMGO is less than half of that of OS.