KuicNet algorithms for blind deconvolution

We show how the recently-developed KuicNet method for instantaneous blind source separation can be extended to the blind deconvolution task. The proposed algorithm has a simple form and is effective in deconvolving source signals with non-zero kurtoses from a linear filtered version of the source se...

Full description

Saved in:
Bibliographic Details
Published inNeural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378) pp. 3 - 12
Main Authors Douglas, S.C., Kung, S.-Y.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1998
Subjects
Blind equalizers
Blind source separation
Convergence
Convolution
Deconvolution
Finite impulse response filter
Nonlinear filters
Principal component analysis
Random variables
USA Councils
Online AccessGet full text

Cover

More Information
Summary:We show how the recently-developed KuicNet method for instantaneous blind source separation can be extended to the blind deconvolution task. The proposed algorithm has a simple form and is effective in deconvolving source signals with non-zero kurtoses from a linear filtered version of the source sequence. We then combine the natural gradient search technique with the KuicNet algorithm to enhance its convergence properties. Simulations verify the useful behavior of the proposed algorithms in deconvolving sources with various distributions.
ISBN:078035060X
9780780350601
ISSN:1089-3555
2379-2329
DOI:10.1109/NNSP.1998.710621