KuicNet algorithms for blind deconvolution

Scott C. Douglas, S. Y. Kung

Research output: Contribution to conferencePaper

10 Scopus citations

Abstract

In this paper, we show how the recently-developed Kuic-Net 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.

Original languageEnglish (US)
Pages3-11
Number of pages9
StatePublished - Jan 1 1998
Externally publishedYes
EventProceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII - Cambridge, Engl
Duration: Aug 31 1998Sep 2 1998

Other

OtherProceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII
CityCambridge, Engl
Period8/31/989/2/98

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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    Douglas, S. C., & Kung, S. Y. (1998). KuicNet algorithms for blind deconvolution. 3-11. Paper presented at Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII, Cambridge, Engl, .