A Novel Adaptive Algorithm for Blind Signal Separation of Linear Mixture

Author

Zhu Yi-yong ; Zhu Yong-gang ; Yao Fu-qiang ; Guan Sheng-yong

Author_Institution

Inst. of Commun. Eng. of PLAUST, Nanjing

fYear

2007

fDate

21-25 Sept. 2007

Firstpage

1028

Lastpage

1031

Abstract

A novel adaptive algorithm for blind separation of the linearly mixed signals based on the relationship of the variability between the source and the mixed signals is presented, which utilizes the generalized eigen-subspace decomposition based on the recursive least square (RLS) parallel computation. The presented algorithm has less mean square error by avoiding the complexity of the estimation of the high-order statistics of the source signal, and it has faster speed of convergence. Simulation results illustrate the efficiency of the algorithm.

Keywords

adaptive signal processing; blind source separation; eigenvalues and eigenfunctions; higher order statistics; least mean squares methods; adaptive algorithm; blind signal separation; eigen-subspace decomposition; less mean square error; linearly mixed signal; recursive least square parallel computation; signal high-order statistics; Adaptive algorithm; Algorithm design and analysis; Biomedical signal processing; Blind source separation; Convergence; Independent component analysis; Least squares methods; Multi-layer neural network; Signal processing algorithms; Source separation;

fLanguage

English

Publisher

ieee

Conference_Titel

Wireless Communications, Networking and Mobile Computing, 2007. WiCom 2007. International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-1311-9

Type

conf

DOI

10.1109/WICOM.2007.263

Filename

4340038