Natural gradient blind deconvolution and equalization using causal FIR filters

Scott C. Douglas, Hiroshi Sawada, Shoji Makino

Research output: Contribution to journalConference articlepeer-review

Abstract

Natural gradient adaptation is an especially convenient method for adapting the coefficients of a linear system in inverse filtering tasks such as blind deconvolution and equalization. Practical implementations of such methods require truncation of the filter impulse responses within the gradient updates. In this paper, we show how truncation of these filter impulse responses can create convergence problems and introduces a bias into the steady-state solution of one such algorithm. We then show how this algorithm can be modified to effectively mitigate these effects for estimating causal FIR approximations to doubly-infinite IIR equalizers. Simulations indicate that the modified algorithm provides the convergence benefits of the natural gradient while still attaining good steady-state performance.

Original languageEnglish
Pages (from-to)197-201
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume1
Publication statusPublished - 2003
Externally publishedYes
EventConference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: 2003 Nov 92003 Nov 12

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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