A statistical noise constrained least mean fourth adaptive algorithm

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

4 Scopus citations

Abstract

In this work, a statistical noise-constrained least mean fourth (SN CLMF) adaptive algorithm is proposed. Based on the fact that in many practical applications an accurate estimate of the fourth-order moment of the noise is available, or can be easily estimated, the learning speed of the LMF algorithm can be then increased considerably by adding a constraint to it. This noise constrained LMF algorithm can be seen as a variable step-size LMF algorithm. Moreover, the concept of energy conservation is used to carry out the rigorous steady-state analysis. Finally, a number of simulations are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF algorithm.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages3817-3820
Number of pages4
DOIs
StatePublished - 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Keywords

  • Adaptive filters
  • Constrained optimization
  • LMF
  • LMS
  • Noise constraints
  • SNCLMF algorithm

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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