最佳线性无偏最大选择CFAR检测算法

为了增强恒虚警检测器对杂波边缘的保护能力,基于提出的最佳线性无偏(BLU)恒虚警检测算法,提出了最佳线性无偏选大恒虚警检测器(BLUGO-CFAR)。它的前、后沿滑窗均采用BLU算法来产生局部估计,将其中的最大值作为检测器对杂波功率水平的估计,去设置自适应检测门限。在SwerlingⅡ型目标及瑞利杂波假设下,推导出了它的P_(fa)、P_d、ADT及杂波边缘虚警尖锋的数学解析表达式。分析结果表明,它在均匀背景及多目标环境中的性能均比GOSGO或OSGO获得了改善,它对杂波边缘的虚警控制能力与控制能力强的GOSGO类似。在特殊情况下,BLUGO退化为MX-CMLD。

A Greatest Selection CFAR Detector Based on the Best Linear Unbiased Estimation

In order to enhance the ability to control the rise of false alarm rate at clutter edges and to obtain the good detection performance in homogeneous background, a new form of the greatest of selection CFAR detector-BLUGO-CFAR is presented in this paper. Its leading and lagging widow both use the best linear unbiased(BLU)method to create two local noise power estimations, the greatest one is selected as a global noise power estimation to set an adaptive detection threshold. Under the assumption of Swerling II target and Rayleigh distributed clutter model, the analytic expressions of Pfa ,Pd, ADT and the peak of false alarm rate at clutter edges are derived. The analytic results show that an improved performance over GOSGO or OSGO is obtained both in homogeneous background and im multiple interfering targets situation, and the ability of BLUGO to control the rise of false alarm rate at clutter edges is as effective as that of GOSGO. In specific cases, BLUGO-CFAR reduces to MX-CMLD.