基于KM-PCA稀疏信号的盲源分离算法

欠定盲信道估计是欠定盲源分离的关键组成部分,其估计精度直接影响到源信号的估计精度.基于充分稀疏假设,在K均值聚类的基础上,提出一种新的欠定盲信道估计算法——K均值与主成分分析方法(KM-PCA算法).该算法首先对观测数据进行K均值聚类,然后对聚类分析结果分别进行主成分分析,修正其聚类中心,从而提高混叠矩阵的估计精度.采...

Blind source separation algorithm based on KM-PCA sparse signal

Underdetermined blind channel estimation is the key element of underdetermined blind source separation and the estimation accuracy will directly affect the estimation accuracy of the source signal.Under the assumption that the sources were fully sparse,a new underdetermined blind channel estimation algorithm—analysis method of K-means and principal component algorithm(KM-PCA) was proposed.This algorithm was firstly used to perform K-means clustering of the observed data.Then the principal component analysis was conducted for the clustering results and the cluster centers were revised,so that the estimation accuracy of mixing matrix was improved.The phonetic signals were simulated and its result showed that KM-PCA algorithm was simple and effective,whose estimation accuracy was superior to that of traditional underdetermined blind channel estimation algorithms.