KuicNet algorithms for blind deconvolution

27 November 2002

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

p. 3-12
https://doi.org/10.1109/nnsp.1998.710621

Abstract

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.

Keywords

DECONVOLUTION
CONVERGENCE
BLIND SOURCE SEPARATION
FINITE IMPULSE RESPONSE FILTER
BLIND EQUALIZERS
PRINCIPAL COMPONENT ANALYSIS
USA COUNCILS
NONLINEAR FILTERS
RANDOM VARIABLES
CONVOLUTION

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