BSS Algorithm by Diffusing Nonparameteric Density Estimator
Nonparametric diffusion mixing estimator (DME) based blind signal separation (BSS) algorithm is proposed under the framework of natural gradient optimization method. In order to improve the performance of signal separation by BSS, the probability distributions of source signals must be described as accurately as possible. In this paper, we use the new data-driven bandwidth selection method based MDE to estimate the probability distributions of sources, which can improve the performance of fixed-width kernel density estimator (FKDE). The MDE is inspired via a Langevin diffusion process. As a result, the proposed algorithm has a wider application and do not need to assume tbe parametric nonlinear functions as them. The effectiveness of tbe proposed algorithm has been confirmed by simulation experiments.
Blind Source Separation(BSS) Independent Component AnalysisfICA) Diffusion Mixing estimator(DME) fixed-width kernel density estimator(FKDE)
Peng Li Rui Li
Department of mathematics North China University of Water Resources and Electric Power Zhenezhou 450 School of Siciences Henan University of Technology Zhengzhou 450052 P.R.China
国际会议
成都
英文
33-36
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）