Blind source separation with nonlinear autocorrelation and non-Gaussianity

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:229

Article

Shi, Zhenwei¹  Jiang, Zhiguo¹  Zhou, Fugen¹  Yin, Jihao¹ 

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

关键词: Blind source separation (BSS);    Independent component analysis (ICA);    Nonlinear autocorrelation;    Non-Gaussianity;   

DOI  :  10.1016/j.cam.2008.10.031

来源: Elsevier

【 摘 要 】

Blind source separation (BSS) is a problem that is often encountered in many applications, such as biomedical signal processing and analysis, speech and image processing, wireless telecommunication systems, data mining, sonar, radar enhancement, etc. One often solves the BSS problem by using the statistical properties of original sources, e.g., non-Gaussianity or time-structure information. Nevertheless, real-life mixtures are likely to contain both non-Gaussianity and time-structure information sources, rendering the algorithms using only one statistical property fail. In this paper, we address the BSS problem when source signals have non-Gaussianity and temporal structure with nonlinear autocorrelation. Based on the two statistical characteristics of sources, we develop an objective function. Maximizing the objective function, we propose a gradient ascent source separation algorithm. Furthermore, We give some mathematical properties for the algorithm. Computer simulations for sources with square temporal autocorrelation and non-Gaussianity illustrate the efficiency of the proposed approach. (C) 2008 Elsevier B.V. All rights reserved.

【 授权许可】

Free