摘要
基于无偏最小方差估计(UMVE)算法,提出了一种新的恒虚警检测器(UMVEM-CFAR)。它的前沿和后沿滑窗均采用UMVE算法来产生局部估计,再对两者求和得到背景功率水平估计。在Swerling II型目标假设下,推导出UMVEM-CFAR在均匀背景下虚警概率Pfa和检测概率Pd及多目标环境下检测概率Pd的解析表达式,与OS-CFAR相比,UMVEM在均匀背景和多目标环境下均具有最好的检测性能,并且它的处理时间只有OS的一半。
A new CFAR detector （ UMVEM-CFAR） based on Unbiased Minimum-variance Estimation （ UMVE） is presented in this paper. It takes the sum of UMVE of leading window and UMVE of lagging window as the global noise power estimation. Under Swerling Ⅱ assumption, the analytic expressions of Pfa and Pd in homogeneous background are derived, and the analytic expression of Pd in multiple target situations is also derived. In contrast to OS-CFAR detector, the UMVEM-CFAR detector has better detection performance in both homogeneous background and multiple target situations. The processing time of the UMVEM- CFAR detector is only half that of the OS-CFAR detector.
出处
《现代雷达》
CSCD
北大核心
2007年第7期38-40,44,共4页
Modern Radar
基金
国家自然科学基金资助项目(60272087)
