Recursive least-squares algorithms of modified Gram-Schmidt type for parallel weight extraction

Author

Sakai, Hideaki

Author_Institution

Div. of Appl. Syst. Sci., Kyoto Univ., Japan

Volume

42

Issue

2

fYear

1994

fDate

2/1/1994 12:00:00 AM

Firstpage

429

Lastpage

433

Abstract

This paper presents some new algorithms for parallel weight extraction in the recursive least-squares (RLS) estimation based on the modified Gram-Schmidt (MGS) method. These are the counterparts of the algorithms using an inverse QR decomposition based on the Givens rotations and do not contain the square root operation. Systolic-array implementations of the algorithms are considered on a 2-D rhombic array. Simulation results are also presented to compare the finite word-length effect of these new algorithms and existing algorithms

Keywords

estimation theory; least squares approximations; parallel algorithms; signal processing; systolic arrays; 2D rhombic array; Givens rotations; RLS estimation; finite word-length effect; inverse QR decomposition; modified Gram-Schmidt method; parallel weight extraction; recursive least-squares algorithms; simulation results; systolic array; Data mining; Equations; Finite wordlength effects; Least squares methods; Matrix decomposition; Recursive estimation; Resonance light scattering; Signal processing algorithms; Systolic arrays; Vectors;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.275620

Filename

275620