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
fDate :
3/1/1999 12:00:00 AM
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;
Journal_Title :
Communications and Networks, Journal of
DOI :
10.1109/JCN.1999.6596694