摘要:
本文基于筛选平均(CM)和无偏筛选平均(UCM)提出了两种改进的恒虚警检测器MCM-CFAR和MUCM-CFAR,并应用了何友(1994)提出的自动筛选技术.在Swerling Ⅱ型目标假设下,并考虑瑞利分布杂波和单脉冲检测情形,本文推导出了MCM-CFAR和MUCM-CFAR检测器的Pfa、Pd和平均判决门限(ADT)的解析表达式,并与其它方案进行了比较.分析结果表明,它们在均匀背景和多目标环境中的性能均明显优于GOSCA和OS;当IL=4,IR=2时,MCM-CFAR比OS改善了2dB,MUCM-CFAR也比OS改善了1.5dB;MCM的性能略优于CM,MUCM与UCM接近,但它们的样本排序时间不足CM、UCM和OS的一半,便于工程实现.
Abstract:
Two modified censored mean (MCM) and modified unbiased censored mean (MUCM) CFAR algorithms are proposed. Both split the reference window into two sub-windows which apply CM or UCM method to create two local noise power estimations, the mean value of them are taken to set an adaptive threshold. Both use the automatic censoring technique proposed by He You (1994). Under Swerling II assumption, considering Rayleigh distributed noise and single-pulse square-law detection, the analytic expressions of Pfa, Pd and ADT of both are derived. By comparison with other schemes, the results show that their performance are evidently superior to that of GOSCA and OS in homogeneous background and in multiple target situations, in the case of IL=4, IR=2, the CFAR loss of MCM is improved by 2 dB relative to that of OS, that of MUCM is improved by 1.5 dB over OS. The performance of MCM is slightly better than that of CM, the performance of MUCM is the nearly same as that of UCM, but their sample sorting time is less than half of that of CM, UCM and OS.
下载:
