P-Norm Based Subband Adaptive Filtering Algorithm

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

Abstract

The normalized subband adaptive filtering algorithm can improve the convergence rate of the normalized least mean square algorithm when dealing with the correlated input signals, but it is plagued by a slow convergence issue in the stable noise. For that reason, the normalized subband p-norm (NSPN) algorithm based on the mean p-power error criterion is proposed in this paper, which shows a fast convergence rate in the α-stable noise. Moreover, by taking advantage of the tap-weights feedback-based convex combination (TFC) scheme, we propose the TFC based NSPN algorithm, which further reaches low steady-state misadjustment under fast convergence. Simulation results have confirmed the superior performance of the proposed algorithms in both system identification and acoustic echo cancellation scenarios.

Original languageEnglish
Title of host publication2023 IEEE 6th International Conference on Electronic Information and Communication Technology, ICEICT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages396-400
Number of pages5
ISBN (Electronic)9798350399059
DOIs
StatePublished - 2023
Event6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023 - Qingdao, China
Duration: 21 Jul 202324 Jul 2023

Publication series

Name2023 IEEE 6th International Conference on Electronic Information and Communication Technology, ICEICT 2023

Conference

Conference6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023
Country/TerritoryChina
CityQingdao
Period21/07/2324/07/23

Keywords

  • convex combination
  • mean p-power error criterion
  • subband adaptive filter
  • system identification

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