Fractional lower order moment (FLOM)-based adaptive algorithm with data-reusing for active noise control of impulsive sources

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper deals with active noise control (ANC) for impulsive noise sources being modeled using non-Gaussian stable process. The filtered-x-LMS (FxLMS) algorithm is based on minimization of the variance of the error signal, and becomes unstable for impulsive noise. The filtered-x least mean p-power (FxLMP) algorithm - based on minimizing the fractional lower order moment (FLOM) - gives robust performance for impulsive ANC; however, its convergence speed is very slow. This paper proposes modifying and employing a generalized normalized LMP (GNLMP) algorithm for impulsive ANC. The proposed approach is based on data-reusing (DR) type adaptive algorithm. The main idea is to improve the stability by efficiently normalizing the step-size, and improve the convergence speed by reusing the recent data. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithm in comparison with the existing algorithms.

Original languageEnglish
Title of host publicationProceedings - 2013 International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2013
Pages31-37
Number of pages7
DOIs
Publication statusPublished - Dec 1 2013
Event2013 9th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2013 - Kyoto, Japan
Duration: Dec 2 2013Dec 5 2013

Publication series

NameProceedings - 2013 International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2013

Conference

Conference2013 9th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2013
CountryJapan
CityKyoto
Period12/2/1312/5/13

Keywords

  • Active noise control
  • Data-reusing
  • Fractional lower order moment (FLOM)
  • Generalized LMP algorithm
  • Non-gaussian stable processes

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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