Algorithm for Nonlinear Blind Source Separation Based on Feature Vector Selection

摘要：

A linear blind source separation algorithm based on generalized eigen-equation resolving is presented. Then a nonlinear blind source separation algorithm is proposed by extending the linear source separation algorithm to the nonlinear domain. The received mixing signals are first mapped to high-dimensional kernel feature space, and a feature vector basis given by the fitness function of the kernel feature space is constructed. Next, in the kernel feature space, the mixing signals are parameterized by the feature vector basis. Finally, the linear blind source separation algorithm based on signal variability is applied to the parameterized mixing signals. The proposed algorithm has simple computation and robustness, and is characterized by high accuracy. Simulation results illustrate well performance on the separation.

关键词： feature vector selection generalized eigen-equation kernel matrix nonlinear mixing

作者: ZHENG Mao ZHANG Wenxi ZHENG Linhua

作者单位: School of Electronic Science and Engineering,National University of Defense Technology,Changsha,4100 Dept.Electronic and Communication Engineering,Changsha University,Changsha,410003,P.R.China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 575-578

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）