基于局部累积概率密度函数估计的CFAR检测门限获取新方法

A Novel Method for CFAR Detection Threshold by Right-tail Local Cumulative Density Function Estimation

摘要: 在恒虚警率（CFAR）检测中，传统基于全局概率密度函数（GPDF）估计获得门限的方法，受模型失配或参数估计误差的影响可能致使恒虚警率性能恶化。为解决该问题提出了基于局部累积概率密度函数（LCDF）估计的非模型化CFAR检测门限获取方法，对影响虚警的杂波幅值较大部分采用简单多项式逼近，根据检测门限、虚警概率、LCDF之间的关系计算门限，最后利用仿真数据和实测数据验证了该方法的有效性。实验结果表明，该方法不仅能保持较好的恒虚警率性能，而且克服了传统方法基于模型假设的局限性。

Abstract: In Constant False Alarm Ratio (CFAR) detection, the traditional method for detection threshold, which is based on Global Probability Density Function (GPDF), may suffer remarkable performance loss due to model mismatch and parameter estimation error. In order to solve this problem, a novel method independent of clutter model is proposed. Without clutter GPDF assumption and parameter estimation, we obtain detection threshold by fitting the right-tail Local Cumulative Density Function (LCDF) with polynomials. Experiments with both simulation and real data confirm good CFAR performance and validity of this method.