HRLS: a more efficient RLS algorithm for adaptive FIR filtering

Author

Woo, Tai-Kuo

Author_Institution

Dept. of Inf. Manage., Nat. Defense Manage. Coll., Taipei, Taiwan

Volume

5

Issue

3

fYear

2001

fDate

3/1/2001 12:00:00 AM

Firstpage

81

Lastpage

84

Abstract

The fast convergence rate and its immunity to the eigenvalue spread of the input correlation matrix make the RLS algorithm particularly attractive. However, the computational complexity is high. We propose using a hierarchical approach to reduce the computational complexity and further increase the convergence rate. The results of simulation runs and theoretical justifications confirm our claims.

Keywords

FIR filters; adaptive Kalman filters; adaptive signal processing; computational complexity; filtering theory; least squares approximations; recursive estimation; recursive filters; HRLS; Kalman RLS algorithm; adaptive FIR filtering; computational complexity reduction; efficient RLS algorithm; eigenvalue spread immunity; fast convergence rate; hierarchical RLS; hierarchical approach; input correlation matrix; simulation results; Adaptive filters; Computational complexity; Computational modeling; Convergence; Eigenvalues and eigenfunctions; Filtering algorithms; Finite impulse response filter; Kalman filters; Mean square error methods; Resonance light scattering;

fLanguage

English

Journal_Title

Communications Letters, IEEE

Publisher

ieee

ISSN

1089-7798

Type

jour

DOI

10.1109/4234.913147

Filename

913147