基于排序数据变率的最大似然恒虚警检测方法

Maximum Likelihood CFAR Detection Based on Ordered Data Variability

在韦布尔杂波背景下,针对在未知数目的目标干扰情况下不能够正确估计杂波统计模型的参数,进而影响雷达对目标的恒虚警检测性能,提出了一种基于排序数据变率的自适应删除最大似然恒虚警检测(ACML-CFAR)。利用基于排序数据变率(ODV)的删除算法对杂波单元进行自适应删除,然后使用其余参考单元采样基于最大似然法来估计杂波分布的参数,并形成检测器的检测门限。仿真结果表明:在均匀杂波下,ACML-CFAR略好于ML-CFAR,表明了ACML-CFAR对于拖尾杂波中的部分较强的杂波尖峰也能够删除;在干扰杂波背景下,能够自适应删除杂波中的干扰,不需要关于干扰目标数的先验信息,克服了ML-CFAR检测器在删除个数小于干扰个数时检测性能严重下降的问题。

In view of the deficiency of the CFAR detection of radar target with unknown number of interfering targets in spiky Weibull clutter environment,an adaptive censoring maximum likelihood constant false alarm rate detector（ACML-CFAR） based on ordered data variability（ODV） was researched in this paper.The ACML-CFAR employed ODV-based censoring algorithm to censor several reference samples,and then employed the rest to estimate the parameters of weibull clutter through maximum likelihood estimator.Through computer simulation,it showed that the detection performance of ACML-CFAR was slightly better than that of ML-CFAR in weibull clutter.In interfering targets environments,when the number of censoring samples was less than the number of interfering targets,the detection performance of ML-CFAR degrade severely.Whereas ACML-CFAR can censor interfering targets adaptively and effectively,and did not need any prior information about the number of interfering targets,and therefore performed robustly.It could be used in actual environment that includeed unknown interfering targets.