Extended fast fixed-order RLS adaptive filters

Author

Merched, Ricardo ; Sayed, Ali H.

Author_Institution

Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA

Volume

49

Issue

12

fYear

2001

fDate

12/1/2001 12:00:00 AM

Firstpage

3015

Lastpage

3031

Abstract

The existing derivations of conventional fast RLS adaptive filters are intrinsically dependent on the shift structure in the input regression vectors. This structure arises when a tapped-delay line (FIR) filter is used as a modeling filter. We show, unlike what original derivations may suggest, that fast fixed-order RLS adaptive algorithms are not limited to FIR filter structures. We show that fast recursions in both explicit and array forms exist for more general data structures, such as orthonormally based models. One of the benefits of working with orthonormal bases is that fewer parameters can be used to model long impulse responses

Keywords

FIR filters; adaptive filters; adaptive signal processing; data structures; delay lines; least squares approximations; recursive filters; convergence rates; data structures; extended RLS adaptive filters; fast fixed-order RLS adaptive algorithms; fast fixed-order RLS adaptive filters; input regression vectors; long impulse response; modeling filter; orthonormal bases; orthonormally based models; shift structure; tapped-delay line FIR filter; Adaptive algorithm; Adaptive filters; Data structures; Echo cancellers; Finite impulse response filter; Lattices; Least squares methods; Recursive estimation; Resonance light scattering; Transversal filters;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.969510

Filename

969510