基于韦布尔分布下改进型恒虚警检测器的检测性能分析

Detection performance analysis of improved constant false alarm rate detector based on Weibull distribution

为了提高恒虚警检测器(constant false alarm rate detector,CFAR)在有干扰目标存在的杂波环境下的检测性能,提出了5种基于自适应删除(automatic censored mean level detector,ACMLD)恒虚警算法与均值类算法的改进型,并在韦布尔分布模型下对影响背景杂波功率估计的参考单元长度和干扰目标个数进行检测概率分析。结果表明:基于该改进型检测器可有效提高有干扰目标的检测性能,其中改进型单侧自适应删除(single automatic censored mean level detector,SACMLD)算法适合在一侧参考窗均匀的情况下使用,而自适应删除最小(adaptive censored smallest of detector,ACSO)算法适合两侧参考窗都不均匀的情况下使用。文中提出的基于韦布尔分布下改进型恒虚警检测器可为铁路、航空等多目标环境检测提供参考。

In order to improve the detection performance of the constant false alarm rate detector(CFAR)in the cluttered environment with jamming targets,five improved CFAR based on automatic censored mean level detector(ACMLD)and mean algorithm are proposed,and the detection probability of reference unit length and the number of jamming targets that affecting the estimation of background clutter power is analyzed under the Weibull distribution model.The results show that the improved detector based on ACMLD and mean class algorithm can effectively improve the detection performance in the environment with jamming targets.The improved single automatic censored mean level detector(SACMLD)algorithm is suitable for the case of uniform reference window on one side.The adaptive censored smallest of detector(ACSO)algorithm is suitable for the case where the reference windows on both sides are uneven.The improved CFAR detector based on Weibull distribution proposed in this paper can provide a reference for the field of multi-target environment detection such as railway and aviation.