Misalignment performance of selective tap adaptive algorithms for system identification of time-varying unknown systems

Patrick A. Naylor*, Andy W.H. Khong, Mike Brookes

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Selective tap algorithms have been proposed as a means of reducing complexity for adaptive filtering. MMax tap selection has been employed in many algorithms due to its straightforward implementation. This paper formulates the analysis of two MMax-based algorithms under time-varying unknown system conditions as are often, found in practical applications. The steady-state misalignment for the MMax normalized, least mean square and the MMax recursive least squares algorithms are derived and their performance is compared to that of their respective full-update algorithms. The tradeoff between computational complexity and misalignment performance is also shown, for the MMax normalized least mean, square case.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesI97-I100
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
ISSN (Print)1520-6149

Conference

Conference2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/TerritoryUnited States
CityHonolulu, HI
Period4/15/074/20/07

ASJC Scopus Subject Areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Acoustic echo cancellation
  • Misalignment performance
  • Partial update adaptive filtering

Fingerprint

Dive into the research topics of 'Misalignment performance of selective tap adaptive algorithms for system identification of time-varying unknown systems'. Together they form a unique fingerprint.

Cite this