Towards Adaptive Blind Extraction of Post-Nonlinearly Mixed Signals

Author

Leong, Wai Yie ; Mandic, Danilo P.

Author_Institution

Dept. of Electron. & Electr. Eng., Imperial Coll. London, London

fYear

2006

fDate

6-8 Sept. 2006

Firstpage

91

Lastpage

96

Abstract

A novel approach which extends blind source extraction (BSE) of one or group of sources to the case of post-nonlinear mixtures is proposed. This is achieved by an adaptive algorithm in which the cost function jointly estimates the kurtosis and a measure of nonlinearity. The analysis of both the quantitative and qualitative performance is provided, and simulation results are presented which illustrate the validity of the proposed approach.

Keywords

adaptive signal processing; blind source separation; estimation theory; adaptive blind extraction; blind source extraction; cost function; kurtosis estimation; post-nonlinearly mixed signals; Adaptive signal processing; Analytical models; Biomedical signal processing; Data mining; Educational institutions; Power system modeling; Sensor phenomena and characterization; Signal processing; Signal processing algorithms; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Conference_Location

Arlington, VA

ISSN

1551-2541

Print_ISBN

1-4244-0656-0

Electronic_ISBN

1551-2541

Type

conf

DOI

10.1109/MLSP.2006.275528

Filename

4053627