基于新概率密度函数的ICA盲源分离  

ICA Blind Signal Separation Based on a New Probability Density Function

作　　者：张娟娟[1] 邸双亮[1] 

机构地区：[1]西安交通大学数学与统计学院,西安710049

出　　处：《工程数学学报》2014年第2期173-180,共8页Chinese Journal of Engineering Mathematics

摘　　要：基于独立分量分析(Independent Component Analysis,ICA),利用极大似然估计法,研究了超高斯和亚高斯的混合信号的盲源分离(Blind Sources Separation,BSS)问题.文中构造了一种新的、不同于以往文章中用来分离混合信号的概率密度函数(Probability Density Function,PDF).新构造的PDF无需改变函数中的参数值,可用来对于超高斯和亚高斯信号的概率密度进行估计(假设未知源信号是相互独立的).数值实验验证了新构造的PDF的可行性,与原算法相比,收敛时间和分离效果都得到了较大的改善.This paper is concerned with the blind source separation （BSS） problem of super-Gaussian and sub-Gaussian mixed signal by using the maximum likelihood method, which is based on independent component analysis （ICA） method. In this paper, we construct a new type of probability density function （PDF） which is different from the already existing PDF used to separate mixed signals in the previously published papers. Applying the new constructed PDF to estimate probability density of super-Gaussian and sub-Gaussian signals （assuming the source signals are independent of each other）, it is not necessary to change the parameter values artificially, and the separation work may be performed adaptively. Numerical experiments verify the feasibility of the newly constructed PDF, and the convergence time and the separation effect are improved compared with the original algorithm.

关 键 词：独立分量分析 极大似然估计 盲源分离 概率密度函数 

分 类 号：TN911.72[电子电信—通信与信息系统]