摘要
在模糊逻辑检测中,利用对应于"有信号"与"无信号"2个隶属度进行判决,比统计检测方法更能充分利用参考单元所提供的信息,提高检测性能。该文将模糊逻辑和单元平均(CA)算法相结合,提出一类新的CFAR检测器,称之为加权平均模糊CFAR检测器(WCAF-CFAR),它的前、后沿滑窗均采用模糊CA方法计算出映射到虚警空间的2个隶属函数值,对这2个值进行加权获得总的背景功率水平估计。GO和SO均可看成是WCAF的特例。仿真结果表明,WCAF-CFAR在均匀背景和非均匀背景下均具有不错的检测性能。
In fuzzy logic detection, the decision is made by the degree of membership to two classes corresponding to the presence or absence of a signal. Thus the observation information is made full used of, and the detection perforce is better than that of statistical detection. A weighted cell averaging and fuzzy rules-CFAR（WCAF-CFAR） detector is proposed in this paper. It computes two membership function values mapped to the false alarm space from leading and lagging reference cells with fuzzy CA method, then obtains a noise power estimation by weighting them. GO-CFAR and SO-CFAR are both regarded as particular cases of WCAF-CFAR. Simulation results show that the WCAFCFAR detector has good detection performance both in homogenous environments and in nonhomogenous environments.
出处
《现代雷达》
CSCD
北大核心
2009年第9期44-46,共3页
Modern Radar
基金
国家自然科学基金资助项目(60802072)
关键词
检测
恒虚警
单元平均
模糊逻辑
隶属函数
detection
constant false alarm rate
cell averaging
fuzzy logic
membership function
