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Indexed by:会议论文

Date of Publication:2011-09-27

Included Journals:EI、Scopus

Page Number:291-296

Abstract:Independent Component Analysis (ICA) is an effective method to solve the problem of Blind Source Separation (BSS). In this paper, a new algorithm is proposed to separate signals mixtured by sub-Gaussian, super-Gaussian, symmetric and asymmetric sources. Alternative score functions in the algorithm are derived by using exponent density model and Gaussian parametric density mixture model. The score functions are selfadaptive through estimating the high-order moments of original signals. Moreover, a stability condition for the proposed algorithm is given to guarantee separating the true solution. Simulations are presented to illustrate the performance and effectiveness of the proposed algorithm. ? 2011 IEEE.