基于最小均方误差和稀疏特征的欠定盲源分离

山东大学学报(工学版)

基于最小均方误差和稀疏特征的欠定盲源分离

白树忠^1,2, 刘琚²,孙国霞²

1. 山东大学电气工程学院, 山东济南 250061;2. 山东大学信息科学与工程学院, 山东济南 250100

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-08-16 发布日期:2008-08-16
通讯作者: 白树忠

An algorithm for under-determined blind source separation based on the least-mean-square error and sparse features

BAI Shu-zhong^1,2, LIU Ju², SUN Guo-xia²

1. School of Electrical Engineering， Shandong University, Jinan 250061, China；2. School of Information Science and Engineering, Shandong University, Jinan 250100, China

Received:1900-01-01 Revised:1900-01-01 Online:2008-08-16 Published:2008-08-16
Contact: BAI Shu-zhong

摘要/Abstract

摘要： 针对欠定条件下的盲源分离问题,即观测信号个数小于信源个数的情况,提出了一种基于最小均方误差和稀疏特征的算法.首先,利用变换后信源的稀疏特征,采用一新的势函数通过聚类算法估计混叠矩阵.然后利用混叠矩阵和信源自身的相关性,通过寻找信源在聚类方向时间点上的精确值,以均方误差最小为准则寻找最佳分离矩阵实现信源的分离,克服了传统的分离算法在寻找最佳分离子矩阵方面的缺点.仿真结果显示使用该方法分离的信号具有更高的信噪比,和其他同类方法相比具有更优越的分离性能.

关键词: 稀疏性, 欠定分离, 最小均方误差

Abstract: An algorithm was presented based on the least-mean-square error and sparse features for under-determined blind source separation, i.e., observed signal numbers are less than sources numbers., Based on clustering method, the mixing matrix was first estimated by a new potential function using the sparseness of sources. By using the estimated mixing matrix and the self-correlation of sources and searching the accurate values at the source clustering directions, the optimal sub-matrix for separation was obtained according to the least-mean-square error criterion. This can overcome the disadvantages of traditional algorithm in searching the optimal sub-matrix. Simulation results show the separated signals have higher SNR, and the proposed approach has better separation performance compared with the other similar methods.

Key words: sparseness, under-determined separation, the least-mean-square error

中图分类号:

TN911.7

白树忠,刘琚,孙国霞 . 基于最小均方误差和稀疏特征的欠定盲源分离[J]. 山东大学学报(工学版), 2008, 38(4): 97-101 .

BAI Shu-zhong ,LIU Ju,SUN Guo-xia .

An algorithm for under-determined blind source separation based on the least-mean-square error and sparse features

[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 97-101 .

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