# [1]白志茂,黄高明,徐琴珍,等.基于信息典型相关分析的盲源分离算法[J].东南大学学报(自然科学版),2009,39(6):1093-1097.[doi:10.3969/j.issn.1001-0505.2009.06.002] 　Bai Zhimao,Huang Gaoming,Xu Qinzhen,et al.Blind source separation algorithm based on information canonical correlation analysis[J].Journal of Southeast University (Natural Science Edition),2009,39(6):1093-1097.[doi:10.3969/j.issn.1001-0505.2009.06.002] 点击复制 基于信息典型相关分析的盲源分离算法() 分享到： var jiathis_config = { data_track_clickback: true };

39

2009年第6期

1093-1097

2009-11-20

## 文章信息/Info

Title:
Blind source separation algorithm based on information canonical correlation analysis

1 东南大学公共卫生学院,南京 210009; 2 海军工程大学电子工程学院,武汉 430033; 3 东南大学信息科学与工程学院,南京 210096
Author(s):
1 School of Public Health, Southeast University, Nanjing 210009, China
2 College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China
3 School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

TN911.7
DOI:
10.3969/j.issn.1001-0505.2009.06.002

Abstract:
To solve the problem of blind source separation, a novel algorithm based on information canonical correlation analysis(ICCA)is presented by combining the theory of mutual information with canonical correlation analysis. In this algorithm, the information canonical vectors are searched out by maximizing the mutual information between the linear combination of the observed vectors and the linear combination of the delayed observed vectors. The probability density function is estimated by Gaussian kernel estimates. Then, the source signals are extracted and separated one by one by multiplying the information canonical vectors with the observed mixture. The simulation results show that this algorithm can separate the mixture signals which consist of super-Gaussian components or of sub-Gaussian components. The mixture signals including these two components and the ill mixture signals can also be separated effectively.

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