摘要
近年来,ICA(Independent Component Analysis,独立成分分析)已成为处理BSS(Blind Source Separation,盲源分离)问题的主要手段,同时也受到人们越来越多的关注。该文首先介绍ICA,然后引入FastICA算法的推导过程,最后通过MATLAB仿真将跳频信号进行盲分离,并与梯度算法所得的仿真结果进行对比分析。通过算法验证,经FastICA处理得到的分离信号与源信号相关系数的绝对值不小于0.99,与梯度算法比较可以明显地得到FastICA是一种更为有效的跳频信号盲分离方法。
ICA（Independent Component Analysis） has been a primary method solving BSS（Blind Source Separation） in recent years,and aroused more and more concern.In this paper,ICA and FastICA algorithm were intrduced,firstly,hop-frequency signals has been separead through MATLAB,and then simulation result by FastICA,gradient algorithm were analyzed.Through verification,absolute value of correlation coefficient between separation signals and source signals is not less than 0.99.Compared with gradient algorithm,the FastICA is a more effective algorithm.
出处
《计算机与数字工程》
2011年第10期64-66,165,共4页
Computer & Digital Engineering
基金
国家自然科学基金(编号:61032001
60972159
61002006)资助
关键词
独立成分分析
跳频信号
盲源分离
梯度算法
independent component analysis
blind source separation
principal component analysis
gradient algorithm
