On the steady‐state performance of bias‐compensated LMS algorithm

Abstract Adaptive filtering algorithms are widespread today owing to their flexibility and simplicity. Due to environments in which they are normally immersed, their robustness against noise has been a topic of interest. Traditionally, in the literature it is assumed that noise is mainly active in t...

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Autores principales:	Rodrigo Pimenta, Leonardo Resende, Mariane R. Petraglia, Diego B. Haddad
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/7fb736fb05df409481b81671f4be1ac7
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id	oai:doaj.org-article:7fb736fb05df409481b81671f4be1ac7
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spelling	oai:doaj.org-article:7fb736fb05df409481b81671f4be1ac72021-11-16T10:15:44ZOn the steady‐state performance of bias‐compensated LMS algorithm1350-911X0013-519410.1049/ell2.12044https://doaj.org/article/7fb736fb05df409481b81671f4be1ac72021-01-01T00:00:00Zhttps://doi.org/10.1049/ell2.12044https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract Adaptive filtering algorithms are widespread today owing to their flexibility and simplicity. Due to environments in which they are normally immersed, their robustness against noise has been a topic of interest. Traditionally, in the literature it is assumed that noise is mainly active in the reference signal. Since this hypothesis is often violated in practice, recently some papers have advanced strategies to compensate the bias introduced by noisy excitation data. The contributions of this paper are twofold. The first one establishes that, in some conditions, the bias‐compensated least mean square algorithm implements an optimum estimator, in the sense that it presents the smallest variance of the set of unbiased estimators. Since the asymptotic mean‐square performance of this algorithm has not yet been investigated in detail, the second contribution adopts an energy conservation relationship to derive its theoretical steady‐state mean squared distortion. The final result is presented in a closed form, is consistent with simulations and is able to provide important guidelines to the designer.Rodrigo PimentaLeonardo ResendeMariane R. PetragliaDiego B. HaddadWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 2, Pp 85-88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Rodrigo Pimenta Leonardo Resende Mariane R. Petraglia Diego B. Haddad On the steady‐state performance of bias‐compensated LMS algorithm
description	Abstract Adaptive filtering algorithms are widespread today owing to their flexibility and simplicity. Due to environments in which they are normally immersed, their robustness against noise has been a topic of interest. Traditionally, in the literature it is assumed that noise is mainly active in the reference signal. Since this hypothesis is often violated in practice, recently some papers have advanced strategies to compensate the bias introduced by noisy excitation data. The contributions of this paper are twofold. The first one establishes that, in some conditions, the bias‐compensated least mean square algorithm implements an optimum estimator, in the sense that it presents the smallest variance of the set of unbiased estimators. Since the asymptotic mean‐square performance of this algorithm has not yet been investigated in detail, the second contribution adopts an energy conservation relationship to derive its theoretical steady‐state mean squared distortion. The final result is presented in a closed form, is consistent with simulations and is able to provide important guidelines to the designer.
format	article
author	Rodrigo Pimenta Leonardo Resende Mariane R. Petraglia Diego B. Haddad
author_facet	Rodrigo Pimenta Leonardo Resende Mariane R. Petraglia Diego B. Haddad
author_sort	Rodrigo Pimenta
title	On the steady‐state performance of bias‐compensated LMS algorithm
title_short	On the steady‐state performance of bias‐compensated LMS algorithm
title_full	On the steady‐state performance of bias‐compensated LMS algorithm
title_fullStr	On the steady‐state performance of bias‐compensated LMS algorithm
title_full_unstemmed	On the steady‐state performance of bias‐compensated LMS algorithm
title_sort	on the steady‐state performance of bias‐compensated lms algorithm
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/7fb736fb05df409481b81671f4be1ac7
work_keys_str_mv	AT rodrigopimenta onthesteadystateperformanceofbiascompensatedlmsalgorithm AT leonardoresende onthesteadystateperformanceofbiascompensatedlmsalgorithm AT marianerpetraglia onthesteadystateperformanceofbiascompensatedlmsalgorithm AT diegobhaddad onthesteadystateperformanceofbiascompensatedlmsalgorithm
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On the steady‐state performance of bias‐compensated LMS algorithm

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