Publication detail

Optimal Step-Size LMS Algorithm Using Exponentially Averaged Gradient Vector

Vladimr Malenovsky, Ing. Zdenek Smekal, Prof., Ing. Ivan Koula, Ing.

Original Title

Optimal Step-Size LMS Algorithm Using Exponentially Averaged Gradient Vector

English Title

Optimal Step-Size LMS Algorithm Using Exponentially Averaged Gradient Vector

Type

conference paper

Language

en

Original Abstract

This letter proposes a new algorithm, which uses an optimal step-size (OSS) weight-adjustment scheme. This strategy leads to a better convergence rate and misadjustment in environments with sudden changes of parameters and for colored input data. The computational complexity is comparable with the well-known RLS. The performance of the novel approach is verified by simulations under system identification scenario and compared with that of the NLMS and RLS algorithms. The strategy uses averaged values of the correlation matrix and the cross-correlation vector. Experimental results for car-interior echo cancelation are presented including analysis of converegnce rate and misadjustment.

English abstract

This letter proposes a new algorithm, which uses an optimal step-size (OSS) weight-adjustment scheme. This strategy leads to a better convergence rate and misadjustment in environments with sudden changes of parameters and for colored input data. The computational complexity is comparable with the well-known RLS. The performance of the novel approach is verified by simulations under system identification scenario and compared with that of the NLMS and RLS algorithms. The strategy uses averaged values of the correlation matrix and the cross-correlation vector. Experimental results for car-interior echo cancelation are presented including analysis of converegnce rate and misadjustment.

Keywords

adaptive filter, least mean square, recursive least squares, convergence rate, computational complexity, misadjustment, optimal step-size, exponential averaging

RIV year

2005

Released

21.11.2005

Publisher

Belgrade, Serbia and Montenegro

Location

Belgrade, Serbia and Montenegro

ISBN

1-4244-0050-3

Book

Proceedings of the Intl. Conference EUROCON 2005

Edition

SVAZEK: R23 SIGNAL PROCESSING

Pages from

1554

Pages to

1557

Pages count

4

BibTex


@inproceedings{BUT15142,
  author="Vladimír {Malenovský} and Zdeněk {Smékal} and Ivan {Koula}",
  title="Optimal Step-Size LMS Algorithm Using Exponentially Averaged Gradient Vector",
  annote="This letter proposes a new algorithm, which uses an optimal step-size (OSS) weight-adjustment scheme. This strategy leads to a better convergence rate and misadjustment in environments with sudden changes of parameters and for colored input data. The computational complexity is comparable with the well-known RLS. The performance of the novel approach is verified by simulations under system identification scenario and compared with that of the NLMS and RLS algorithms. The strategy uses averaged values of the correlation matrix and the cross-correlation vector. Experimental results for car-interior echo cancelation are presented including analysis of converegnce rate and misadjustment.",
  address="Belgrade, Serbia and Montenegro",
  booktitle="Proceedings of the Intl. Conference EUROCON 2005",
  chapter="15142",
  edition="SVAZEK: R23 SIGNAL PROCESSING",
  institution="Belgrade, Serbia and Montenegro",
  year="2005",
  month="november",
  pages="1554",
  publisher="Belgrade, Serbia and Montenegro",
  type="conference paper"
}