Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17564
Title: A nonlinear recursive least-squares algorithm for the blind separation of finite-alphabet sources
Authors: Douglas, SC
Kung, SY
Keywords: Adaptive signal processing
Least squares approximations
Noise
Recursive estimation
Source separation
Issue Date: 2003
Publisher: IEEE
Source: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing : proceedings : April 6-10, 2003, Hong Kong Exhibition and Convention Centre, Hong Kong, v. 2, p. II729-II732 How to cite?
Abstract: We present an adaptive algorithm that blindly separates mixtures of finite-alphabet sources given knowledge of the source alphabet and distribution. The algorithm is a nonlinear recursive least-squares procedure that employs a simple and numerically-robust square root Householder update. Simulations verify that the algorithm can separate large-scale noisy mixtures of finite-alphabet sources without any knowledge of the number of sources in the mixture.
URI: http://hdl.handle.net/10397/17564
ISBN: 0-7803-7663-3
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2003.1202470
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

26
Last Week
4
Last month
Checked on Aug 14, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.