A New CFAR Detector Based on PDF Estimation of Clutter Using Maximum Entropy Method

摘要：

A new CFAR (constant false alarm rate) algorithm is proposed for the Blind-CFAR detector which is proposed for CFAR detection in various types of clutter. The new algorithm employs the maximum entropy (MaxEnt) method to estimate the PDF (probability density function) of clutter and thus the CFAR detection thresholds based on the samples of reference cells and some moment constraints. Performance of the new algorithm is proved to converge to the performance of the ideal. detector as the numbers of the reference cells and the moment constraints approach to infinity. With finite reference cells and moment constraints, performance of the new algorithm is compared with that of the maximum likelihood (ML) CFAR detector in uniform Weibull clutter with known and mismatched shape parameters. The results show that, the Blind-CFAR detector employing the new algorithm performs as well as the ML-CFAR detector in case of known shape parameters and it outperforms the ML-CFAR detector in case of mismatched shape parameters.

关键词： radar CFAR detector maximum entropy method Weibull clutter

作者: Jun Li Xuesong Wang Tao Wang

作者单位: School of Electronic Science and Engineering National University of Defense Technology Changsha 410073, China

会议类型: 国际会议

会议名称: 2010 International Conference on Signal and Information Processing(2010年IEEE信号与信息处理国际会议 ICSIP2010)

会议地点: 长沙

会议语种:英文

页码: 132-136

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）