摘要
盲源分离(BSS)算法通常需预先假设源信号的概率密度函数(PDF),并由此获得关键的激活函数(AF),进而从混合信号中分离出源信号。但若假设的概率密度函数与真实概率密度函数差异较大,源信号将不能被正确分离。基于峰度的盲源分离开关算法无需假设源信号的概率密度函数,可直接对独立分量分析(ICA)中的激活函数进行自适应学习。计算机仿真证明,该算法可有效进行盲源分离。
In blind source separation (BSS), the general methods firstly assume the probability density function (PDF) of sources to obtain the important activation function (AF), and then separate the source signal from mixture signals. If the assumed PDF is different from the true PDF considerably, the sources will not be separated correctly. Switching algorithm of blind source separation based on kurtosis is used to adaptively learn activation function of the independent component analysis (ICA) without assuming the PDF of sources. Computer simulation shows that this algorithm can separate sources effectively.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2005年第1期185-188,206,共5页
Journal of System Simulation
关键词
盲源分离
独立分量分析
激活函数
峰度
开关算法
blind source separation
independent component analysis
activation function
kurtosis
switching algorithm
