Trimmed-Mean FxLMS Algorithm for ANC in Impulsive Noise

Dah Chung Chang, Po Wei Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The filtered-x least mean square (FxLMS) algorithm has been widely used for active noise control (ANC), which has a satisfying convergence performance in the Gaussian noise. Nevertheless, the ANC system can receive an unusual signal contaminated by the impulsive noise (IN) in practical applications, which results in serious convergence performance degradation. Some references can be found discussing this issue, but many of the proposed solutions highly rely on the setup of pre-determined parameters depending on special situations. In this paper, the IN problem is dealt with by some robust algorithms, which are adapted from order statistics. With the new FxLMS algorithms, there is no pre-requisite to determine the parameters as required in the past works. Through simulation results, we show that the proposed method has better average noise reduction convergence performance compared to other robust methods.

Original languageEnglish
Title of host publication2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1268-1271
Number of pages4
ISBN (Electronic)9789881476852
DOIs
StatePublished - 4 Mar 2019
Event10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
Duration: 12 Nov 201815 Nov 2018

Publication series

Name2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Country/TerritoryUnited States
CityHonolulu
Period12/11/1815/11/18

Keywords

  • Active noise control
  • FxLMS
  • Impulsive noise
  • Order statistics
  • Robust algorithm

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