• 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