一种新的基于UMVE和OS的恒虚警检测算法

为了充分利用参考单元所提供的信息,减少恒虚警损失,该文基于无偏最小方差估计(UMVE)方法和有序统计(OS)方法,提出了一种新的恒虚警检测器(MOSUM-CFAR).它的前沿和后沿滑窗分别采用UMVE和OS方法产生两个局部估计,再对二者求和得到背景功率水平估计.在SwerlingⅡ型目标假设下,文中推导出MOSUM-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,并与其它方案作了比较.分析结果表明MOSUM-CFAR在均匀背景和多目标环境下均具有相当好的检测性能.

A Noval CFAR Algorithm Based on Unbiased Minimum-Variance Estimation and Ordered Statistics Estimation

In order to make full use of the cell information and decrease CFAR loss, A new CFAR detector (MOSUM-CFAR) based on unbiased minimum-variance estimation and ordered statistics estimation is presented in this paper . It takes the sum of UMVE of leading window and OS estimation of lagging window as a global noise power estimation. Under swerling II assumption, the analytic expressions of Pf aand Pd in homogeneous background are derived, and the analytic expression of Pd in multiple target situations is also derived. In contrast to other detectors, the MOSUM-CFAR detector has fairly well detection performance in both homogeneous background and multiple target situations.