New FxLMAT-Based Algorithms for Active Control of Impulsive Noise

Alina Mirza, Farkhanda Afzal, Ayesha Zeb, Abdul Wakeel, Waqar Shahid Qureshi, Ali Akgul

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In the presence of non-Gaussian impulsive noise (IN) with a heavy tail, active noise control (ANC) algorithms often encounter stability problems. While adaptive filters based on the higher-order error power principle have shown improved filtering capability compared to the least mean square family algorithms for IN, however, the performance of the filtered-x least mean absolute third (FxLMAT) algorithm tends to degrade under high impulses. To address this issue, this paper proposes three modifications to enhance the performance of the FxLMAT algorithm for IN. To improve stability, the first alteration i.e. variable step size FxLMAT (VSSFxLMAT)algorithm is suggested that incorporates the energy of input and error signal but has slow convergence. To improve its convergence, the second modification i.e. filtered x robust normalized least mean absolute third (FxRNLMAT) algorithm is presented but still lacks robustness. Therefore, a third modification i.e. modified filtered-x RNLMAT (MFxRNLMAT) is devised, which is relatively stable when encountered with high impulsive noise. With comparable computational complexity, the proposed MFxRNLMAT algorithm gives better robustness and convergence speed than all variants of the filtered-x least cos hyperbolic algorithm, and filtered-x least mean square algorithm.

Original languageEnglish
Pages (from-to)81279-81288
Number of pages10
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • Adaptive signal processing
  • mean noise reduction
  • non-Gaussian

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