一种基于ICA的盲信号分离快速算法

基于ICA(独立成分分析:Independent Component Analylsis)原则,给出一种盲信号分离的快速学习算法.通过寻求观测变量线性组合的四阶累积量(即kurtosis系数)局部极值,得出该算法的模型和步骤.将该算法用于盲信号分离实验,实验结果表明,该算法在盲信号分离和信号特征提取方面具有收敛速度快、无需动态参数等优点.该算法能有效地分离出任意分布的非高斯盲源信号的各个独立成分,是信号处理的一种新的、高效可靠的方法.

A Fast Algorithm of Blind Signal Separation Based on ICA

Based on ICA (Independent Component Analylsis) principle,a fast leaning algorithm for blind signal separation is presented.By seeking the local extrema of the fourth-order cumulants (i.e. kurtosis coefficients) of a linear combination of the observed variables, the model and the process of this algorithm are obtained and then used for the experiment of blind signal separation. The results of the experiment show that this algorithm has a great deal advantages in blind signal separation and features extraction such as fast convergence and needless in any dynamic parameter and the like.The algorithm can separates all independent components from blind source signals,which is non-Gaussian distribution.The algorithm is a new,highly efficient and reliable method in signal processing.