基于高斯化-广义匹配的脉冲型噪声处理方法研究  

A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching

作　　者：罗忠涛 卢鹏 张杨勇[2] 张刚[1] LUO Zhongtao;LU Peng;ZHANG Yangyong;ZHANG Gang(Chongqing Key Laboratory of Signal and Information Processing,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Wuhan Maritime Communication Research Institute,Wuhan 430079,China)

机构地区：[1]重庆邮电大学信号与信息处理重庆市重点实验室,重庆400065 [2]武汉船舶通信研究所,武汉430079

出　　处：《电子与信息学报》2018年第12期2928-2935,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61701067;61771085;61671095);重庆市教育委员会科研基金(KJ1600427;KJ1600429)~~

摘　　要：针对脉冲型噪声,该文提出一种新的非线性处理方法,即高斯化-广义匹配(GGM)处理。GGM方法基于高斯化处理与广义匹配滤波,可结合非参数的概率密度估计进行设计,解决噪声模型未知时的非线性处理问题。该文以脉冲型噪声分布模型为例,分析GGM方法的特点和性能;再结合Class A噪声模型,讨论GGM设计作为非参数方法相比模型假设失配的优势;引入效能函数,验证GGM方法在恒虚警技术中的运用。结果表明,在已知噪声分布情况下,GGM方法具有次优检测性能;当噪声模型未知时,非参数GGM设计能保持稳健性能,优于模型失配下的处理。并且,GGM设计对样本数目要求不高,为噪声特性不明或时变的场景提供了一种新的信号处理方法。A method based on Gaussianization and generalized matching, called Gaussianization-Generalized Matching （GGM） method is proposed, for nonlinear processing in impulsive noise. The GGM method can be designed based on noise samples, aided by nonparametric probability density estimation. Thus the GGM design is suitable for nonlinear processing in unknown noise models. The GGM method in the SαS model is analyzed, and also the comparison with another approach is presented based on unmatched noise model assumption in the Class A noise. The GGM method is applied to the constant false alarm rate technique via the efficacy function. Simulation and analysis results show that the GGM design is sub-optimal, works robustly when the noise model is unknown, and raises a low requirement on the sample number. Thus, the GGM method provides a promising choice when the noise model is unclear or time-varying.

关 键 词：脉冲型噪声 非线性处理 高斯化处理 广义匹配滤波 效能函数 

分 类 号：TN911[电子电信—通信与信息系统]