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Adaptive noise reduction using numerically stable fast recursive least squares algorithm
Author(s) -
Ykhlef Farid,
Arezki Madjid,
Guessoum Abderezzak,
Berkani Daoud
Publication year - 2007
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.926
Subject(s) - recursive least squares filter , reduction (mathematics) , algorithm , adaptive filter , noise (video) , stability (learning theory) , wiener filter , noise reduction , mathematics , least squares function approximation , numerical stability , filter (signal processing) , adaptive algorithm , control theory (sociology) , computer science , numerical analysis , statistics , artificial intelligence , mathematical analysis , geometry , control (management) , machine learning , estimator , image (mathematics) , computer vision
This paper is concerned with adaptive noise reduction based on the fast recursive least squares (FRLS) algorithm. It is well known that the fast recursive least squares (FRLS) algorithm suffers from numerical instability when operating under the effects of finite precision arithmetic. Several numerical solutions of stabilization were proposed in the case of stationary signals. In this work a new version of a numerically stable FRLS algorithm (NS‐FRLS) is proposed. The stability characteristics of this new stabilized algorithm are analysed. The analysis is based on a linear model for the errors in the states of the adaptive filter. Experimental results confirm the merits of adaptive filtering with the NS‐FRLS algorithm over optimum filtering using the solution provided by Wiener–Hopf equations. Copyright © 2006 John Wiley & Sons, Ltd.