On whitening for Krylov-proportionate normalized least-mean-square algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The contributions of this paper are twofold. The first is to give theoretical motivation for whitening in the recently proposed adaptive filtering algorithm named the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm. The second is to present the details of whitening in KPNLMS (In the original work of KPNLMS, the whitening procedure is mentioned but is not described in detail). An interesting connection among the transform-domain adaptive filter (TDAF), proportionate normalized least-mean-square (PNLMS), and KPNLMS algorithms is also provided. Numerical examples demonstrate that KPNLMS drastically outperforms TDAF especially in noisy situations.

Original languageEnglish
Title of host publicationProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages315-320
Number of pages6
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
Duration: 2008 Oct 162008 Oct 19

Publication series

NameProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Other

Other2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
CountryMexico
CityCancun
Period08/10/1608/10/19

Keywords

  • Adaptive filter
  • Krylov subspace
  • Proportionate NLMS
  • Whitening

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Electrical and Electronic Engineering

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