DocumentCode
1335202
Title
An unsupervised hybrid network for blind separation of independent non-Gaussian source signals in multipath environment
Author
Choi, Seungjin ; Cichocki, Andrzej
Author_Institution
School of Electrical and Electronics Engineering, ChungBuk National University, Korea
Volume
1
Issue
1
fYear
1999
fDate
3/1/1999 12:00:00 AM
Firstpage
19
Lastpage
25
Abstract
This paper is concerned with the problem of recovering multiple source signals that are transmitted through a linear Multiple Input Multiple Output (MIMO) system, without resorting to any prior knowledge. Source signals are assumed to be spatially independent and temporally i.i.d. non-Gaussian sequences. We present an unsupervised hybrid network (a linear feedback network with FIR synapses followed by a linear memoryless feedforward network) which is able to recover multiple source signals blindly. A simple criterion for multichannel blind deconvolution and an associated learning algorithm are presented. Extensive computer simulation results confirm the validity and high performance of the proposed method.
Keywords
Blind source separation; Deconvolution; Decorrelation; Feedforward neural networks; Finite impulse response filters; MIMO; Vectors; Blind signal separation; Hebbian/anti-Hebbian learning; independent component analysis; multichannel blind deconvolution/equalization; neural networks; spatio-temporal decorrelation; unsupervised learning;
fLanguage
English
Journal_Title
Communications and Networks, Journal of
Publisher
ieee
ISSN
1229-2370
Type
jour
DOI
10.1109/JCN.1999.6596694
Filename
6596694
Link To Document