Title :
A novel fast algorithm for blind signal separation
Author :
Zhou, Weidong ; Jia, Lei
Author_Institution :
Sch. of Inf. Sci., Shandong Univ., Jinan, China
Abstract :
The independent component analysis (ICA) for blind source separation is investigated in this paper. A contrast function is given based on maximum likelihood and mutual information theory. A fast iterative ICA algorithm is derived by optimizing the function. The method does not need to calculate the higher order statistics of signals, and has the property of second-order convergence. The experimental results for separation of simulation and real signals using the proposed algorithm is presented.
Keywords :
convergence; information theory; maximum likelihood estimation; neural nets; principal component analysis; signal processing; blind signal separation algorithm; blind source separation; contrast function; experimental results; function optimisation; independent component analysis; iterative ICA algorithm; maximum likelihood; mutual information theory; neural networks; second-order convergence; Blind source separation; Data mining; Entropy; Independent component analysis; Iterative algorithms; Mutual information; Neural networks; Random variables; Signal processing algorithms; Source separation;
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
DOI :
10.1109/WCICA.2002.1021437
