New data-reusing LMS algorithms for improved convergence

Author

Schnaufer, Bemard A. ; Jenkins, W. Kenneth

Author_Institution

Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA

fYear

1993

fDate

1-3 Nov 1993

Firstpage

1584

Abstract

A geometric framework is adopted which is used to clearly elucidate the operation of and relationships between the LMS, DR-LMS, and NLMS algorithms. This geometrical framework facilitates the proof of an analytical result which explains the superior convergence rate performance of the NLMS algorithm. A new class of computationally efficient data-reusing algorithms is then introduced which provides significant convergence rate improvement over the DR-LMS algorithm. The improved performance is verified with simulations

Keywords

adaptive filters; convergence of numerical methods; digital filters; filtering and prediction theory; least squares approximations; DR-LMS; LMS; NLMS algorithms; adaptive filtering; convergence; convergence rate performance; data-reusing LMS algorithms; geometric framework; simulations; Adaptive filters; Algorithm design and analysis; Colored noise; Computational modeling; Convergence; Equations; Filtering algorithms; Finite impulse response filter; Least squares approximation; Performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-4120-7

Type

conf

DOI

10.1109/ACSSC.1993.342346

Filename

342346