Bias‐compensated FX‐LMS algorithm Article Swipe

PDF

Related Concepts

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

Luciana Pereira Silva , Jose Victor Goncalez de Souza , J. Colares , Amaro A. de Lima , Diego B. Haddad ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1049/el.2020.2010 · OA: W3096085145

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.