Adaptive multichannel blind deconvolution using state-space models

Andrzej Cichocki, Liqing Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Independent component analysis (ICA) and related problems of blind source separation (BSS) and multichannel blind deconvolution (MBD) problems have recently gained much interest due to many applications in biomedical signal processing, wireless communications and geophysics. In this paper both linear and nonlinear state space models for blind and semi-blind deconvolution are proposed. New unsupervised adaptive learning algorithms performing extended linear multichannel blind deconvolution are developed. For a nonlinear mixture, a hyper radial basis function (HRBF) neural network is employed and associated supervised-unsupervised learning rules for its parameters are developed. Computer simulation experiments confirm the validity and performance of the developed models and associated learning algorithms.

Original languageEnglish
Title of host publicationProceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages296-299
Number of pages4
ISBN (Electronic)0769501400, 9780769501406
DOIs
Publication statusPublished - 1999
Externally publishedYes
Event1999 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999 - Caesarea, Israel
Duration: 14 Jun 199916 Jun 1999

Publication series

NameProceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999

Conference

Conference1999 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999
Country/TerritoryIsrael
CityCaesarea
Period14/06/9916/06/99

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