Sign subband adaptive filter with selection of number of subbands

Jae Jin Jeong, Seung Hun Kim, Gyogwon Koo, Sang Woo Kim

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

1 Scopus citations

Abstract

The sign subband adaptive filter (SSAF) algorithm is introduced to reduce performance degradation of leastmean-square-type algorithms due to a correlated input signal or an impulsive noise environments. However, this algorithmh has huge computational complexity when the length of the unknown system is large. In this paper, we focus on reduce computational complexity of the conventional SSAF algorithm and propose an SSAF algorithm which selects number of subbands according to convergence state. The specific bands which contributes to decrease the mean-square deviation are used to update the adaptive filter. Thus, the proposed algorithm reduces the computational complexity compared to the conventional SSAF algorithm. The selection mehtod is derived by analysing the mean-square deviation. Through the computer simulation, simulation results are presented that demonstrate the fast convergence rate of the proposed algorithm and save the computational cost.

Original languageEnglish
Title of host publicationICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings
EditorsJoaquim Filipe, Joaquim Filipe, Kurosh Madani, Oleg Gusikhin, Jurek Sasiadek
PublisherSciTePress
Pages407-411
Number of pages5
ISBN (Electronic)9789897581229
DOIs
StatePublished - 2015
Event12th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2015 - Colmar, Alsace, France
Duration: 21 Jul 201523 Jul 2015

Publication series

NameICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings
Volume1

Conference

Conference12th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2015
Country/TerritoryFrance
CityColmar, Alsace
Period21/07/1523/07/15

Keywords

  • Adaptive filter
  • Impulsive noise
  • Mean-square deviation
  • Sign algorithm

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