Projection methods for improved performance in FIR adaptive filters

R. A. Soni, K. A. Gallivan, W. K. Jenkins

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

3 Citations (Scopus)

Abstract

The normalized LMS algorithms offer low-computational complexity and inexpensive implementations for FIR adaptive filters. However, convergence rate decreases as the eigenvalue ratio (condition number) of the input autocorrelation matrix increases. Recursive least squares methods offer significant convergence rate improvement but at the expense of increased computational complexity. In this paper, we present a class of algorithms, collectively called Projection Methods, which offers flexibility in the tradeoff between computational complexity and convergence rate improvement. These methods are related to traditional normalized data reusing algorithms described by Schnaufer and Jenkins. Utilizing conjugate gradient and Tchebyshev methods, algorithms are developed which accelerate the convergence behavior of traditional normalized data reusing algorithms while maintaining excellent tracking performance.

Original languageEnglish (US)
Title of host publicationMidwest Symposium on Circuits and Systems
Editors Anon
PublisherIEEE
Pages746-749
Number of pages4
Volume2
StatePublished - 1997
EventProceedings of the 1997 40th Midwest Symposium on Circuits and Systems. Part 1 (of 2) - Sacramento, CA, USA
Duration: Aug 3 1997Aug 6 1997

Other

OtherProceedings of the 1997 40th Midwest Symposium on Circuits and Systems. Part 1 (of 2)
CitySacramento, CA, USA
Period8/3/978/6/97

Fingerprint

FIR filters
Adaptive filters
Computational complexity
Autocorrelation

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Cite this

Soni, R. A., Gallivan, K. A., & Jenkins, W. K. (1997). Projection methods for improved performance in FIR adaptive filters. In Anon (Ed.), Midwest Symposium on Circuits and Systems (Vol. 2, pp. 746-749). IEEE.
Soni, R. A. ; Gallivan, K. A. ; Jenkins, W. K. / Projection methods for improved performance in FIR adaptive filters. Midwest Symposium on Circuits and Systems. editor / Anon. Vol. 2 IEEE, 1997. pp. 746-749
@inproceedings{e4748d6dede04c55b9104628e1860277,
title = "Projection methods for improved performance in FIR adaptive filters",
abstract = "The normalized LMS algorithms offer low-computational complexity and inexpensive implementations for FIR adaptive filters. However, convergence rate decreases as the eigenvalue ratio (condition number) of the input autocorrelation matrix increases. Recursive least squares methods offer significant convergence rate improvement but at the expense of increased computational complexity. In this paper, we present a class of algorithms, collectively called Projection Methods, which offers flexibility in the tradeoff between computational complexity and convergence rate improvement. These methods are related to traditional normalized data reusing algorithms described by Schnaufer and Jenkins. Utilizing conjugate gradient and Tchebyshev methods, algorithms are developed which accelerate the convergence behavior of traditional normalized data reusing algorithms while maintaining excellent tracking performance.",
author = "Soni, {R. A.} and Gallivan, {K. A.} and Jenkins, {W. K.}",
year = "1997",
language = "English (US)",
volume = "2",
pages = "746--749",
editor = "Anon",
booktitle = "Midwest Symposium on Circuits and Systems",
publisher = "IEEE",

}

Soni, RA, Gallivan, KA & Jenkins, WK 1997, Projection methods for improved performance in FIR adaptive filters. in Anon (ed.), Midwest Symposium on Circuits and Systems. vol. 2, IEEE, pp. 746-749, Proceedings of the 1997 40th Midwest Symposium on Circuits and Systems. Part 1 (of 2), Sacramento, CA, USA, 8/3/97.

Projection methods for improved performance in FIR adaptive filters. / Soni, R. A.; Gallivan, K. A.; Jenkins, W. K.

Midwest Symposium on Circuits and Systems. ed. / Anon. Vol. 2 IEEE, 1997. p. 746-749.

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

TY - GEN

T1 - Projection methods for improved performance in FIR adaptive filters

AU - Soni, R. A.

AU - Gallivan, K. A.

AU - Jenkins, W. K.

PY - 1997

Y1 - 1997

N2 - The normalized LMS algorithms offer low-computational complexity and inexpensive implementations for FIR adaptive filters. However, convergence rate decreases as the eigenvalue ratio (condition number) of the input autocorrelation matrix increases. Recursive least squares methods offer significant convergence rate improvement but at the expense of increased computational complexity. In this paper, we present a class of algorithms, collectively called Projection Methods, which offers flexibility in the tradeoff between computational complexity and convergence rate improvement. These methods are related to traditional normalized data reusing algorithms described by Schnaufer and Jenkins. Utilizing conjugate gradient and Tchebyshev methods, algorithms are developed which accelerate the convergence behavior of traditional normalized data reusing algorithms while maintaining excellent tracking performance.

AB - The normalized LMS algorithms offer low-computational complexity and inexpensive implementations for FIR adaptive filters. However, convergence rate decreases as the eigenvalue ratio (condition number) of the input autocorrelation matrix increases. Recursive least squares methods offer significant convergence rate improvement but at the expense of increased computational complexity. In this paper, we present a class of algorithms, collectively called Projection Methods, which offers flexibility in the tradeoff between computational complexity and convergence rate improvement. These methods are related to traditional normalized data reusing algorithms described by Schnaufer and Jenkins. Utilizing conjugate gradient and Tchebyshev methods, algorithms are developed which accelerate the convergence behavior of traditional normalized data reusing algorithms while maintaining excellent tracking performance.

UR - http://www.scopus.com/inward/record.url?scp=0031380396&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031380396&partnerID=8YFLogxK

M3 - Conference contribution

VL - 2

SP - 746

EP - 749

BT - Midwest Symposium on Circuits and Systems

A2 - Anon, null

PB - IEEE

ER -

Soni RA, Gallivan KA, Jenkins WK. Projection methods for improved performance in FIR adaptive filters. In Anon, editor, Midwest Symposium on Circuits and Systems. Vol. 2. IEEE. 1997. p. 746-749