Open AccessBias‐compensated FX‐LMS algorithm
Author(s) -
Silva L.P.R.,
Souza J.V.G.,
Colares J.,
Lima A.A.,
Haddad D.B.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.2010
Subject(s) - least mean squares filter , active noise control , adaptive filter , noise (video) , algorithm , recursive least squares filter , control theory (sociology) , computer science , filter (signal processing) , adaptive algorithm , signal (programming language) , mathematics , control (management) , artificial intelligence , image (mathematics) , computer vision , programming language
Active noise control is an expanding field that requires a suitable synthesis of secondary perturbations. Unfortunately, most schemes for noise cancelling do not take into account that the input signal that drives the adaptive filter can be noisy. In this Letter, it is theoretically shown that noise perturbations in the excitation data degrade the performance of the standard filtered‐x least mean squares (FX‐LMS) algorithm. Furthermore, a method that compensates such an issue is devised, and a first‐order stochastic analysis of the resulting algorithm is performed. The results reveal that the proposed scheme outperforms the standard FX‐LMS algorithm, even when the variance of the additive noise in the input is not accurately estimated.