Blind source extraction based on generalized autocorrelations and complexity pursuit

Authors:
Hongjuan Zhang;Zhenwei Shi;Chonghui Guo
Affiliations:
Department of Mathematics, Shanghai University, Shanghai 200444, PR China;Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, PR China;Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, PR China
Venue:
Neurocomputing
Year:
2009

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Abstract

Blind source extraction (BSE) is a special class of blind source separation (BSS) method. Due to its low computation load and fast processing speed, BSE has become one of the promising methods in signal processing and analysis. This paper addresses BSE problem when a desired source signal has temporal structures. Based on the generalized autocorrelations of the desired signal and the non-Gaussianity of its innovations, we develop an objective function. Maximizing this objective function, we present a BSE algorithm and further give its stability analysis in this paper. Simulations on image data and electrocardiogram (ECG) data indicate its better performance and the better property of tolerance to the estimation error of the time delay.