Title :
Extraction of Periodic Signals Based on Maximum Likelihood Estimation
Author :
Li, Yunxia ; Fan, Changyuan
Author_Institution :
Sch. of Autom., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
In many applications, such as biomedical signal processing, it is often required to obtain specific periodic source signals. In this paper, we propose a new method based on maximum likelihood estimation to extract periodic signals from linear mixtures. Maximum likelihood function is firstly given according to the periodic property of the desired signals. Then the updating rules for extraction vector are deduced. The derived algorithm is computationally simple, since it is based only on the second order statistical information. Simulation results are given to show its good performance.
Keywords :
blind source separation; higher order statistics; maximum likelihood estimation; biomedical signal processing; blind source extraction; blind source separation; extraction vector; fetal extraction; linear mixtures; maximum likelihood estimation; maximum likelihood function; periodic signal extraction; periodic source signals; second order statistical information; Biomedical signal processing; Blind source separation; Data mining; Independent component analysis; Maximum likelihood estimation; Signal processing; Signal processing algorithms; Source separation; Statistics; Vectors; blind source extraction; blind source separation; fetal extraction;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.534