Based on the generalization of the central limit theorem(CLT) to special dependent variables, this paper shows that maximization of the nonGaussianity(NG) measure can separate the statistically dependent source signal...Based on the generalization of the central limit theorem(CLT) to special dependent variables, this paper shows that maximization of the nonGaussianity(NG) measure can separate the statistically dependent source signals, and the novel NG measure is given by Cook's Euclidean distance using the Chebyshev-Hermite series expansion. Then, a novel blind source separation (BSS) algorithm for linear mixed signals is proposed using Cook's NG measure, which makes it possible to separate statistically dependent source signals. Moreover, the proposed separation algorithm can result in the famous FastICA algorithm. Simulation results show that the proposed separation algorithm is able to separate the dependent signals and yield ideal展开更多
基金The National Natural Science Foundation of China (No.60672049)the Science Foundation of Henan University of Technolo-gy(No.06XJC032)
文摘Based on the generalization of the central limit theorem(CLT) to special dependent variables, this paper shows that maximization of the nonGaussianity(NG) measure can separate the statistically dependent source signals, and the novel NG measure is given by Cook's Euclidean distance using the Chebyshev-Hermite series expansion. Then, a novel blind source separation (BSS) algorithm for linear mixed signals is proposed using Cook's NG measure, which makes it possible to separate statistically dependent source signals. Moreover, the proposed separation algorithm can result in the famous FastICA algorithm. Simulation results show that the proposed separation algorithm is able to separate the dependent signals and yield ideal
