A fixed-point algorithm for blind source separation with nonlinear autocorrelation

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:223

Article

Shi, Zhenwei¹  Jiang, Zhiguo¹  Zhou, Fugen¹ 

[1] Beijing Univ Aeronaut & Astronaut, Image Proc Ctr, Sch Astronaut, Beijing 100083, Peoples R China

关键词: Blind source separation (BSS);    Independent component analysis (ICA);    Nonlinear autocorrelation;    Fixed-point algorithm;   

DOI  :  10.1016/j.cam.2008.03.009

来源: Elsevier

【 摘 要 】

This paper addresses blind source separation (BSS) problem when source signals have the temporal structure with nonlinear autocorrelation. Using the temporal characteristics of sources, we develop an objective function based on the nonlinear autocorrelation of sources. Maximizing the objective function, we propose a fixed-point source separation algorithm. Furthermore, we give some mathematical properties of the algorithm. Computer simulations for sources with square temporal autocorrelation and the real-world applications in the analysis of the magnetoencephalographic recordings (MEG) illustrate the efficiency of the proposed approach. Thus, the presented BSS algorithm, which is based on the nonlinear measure of temporal autocorrelation, provides a novel statistical property to perform BSS. (C) 2008 Elsevier B.V. All rights reserved.

【 授权许可】

Free