Convergence and tracking analysis of a constrained least mean fourth adaptive algorithm

Syed Ali Aamir Imam, Azzedine Zerguine, Muhammad Moinuddin

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

Abstract

It is a well established fact that the addition of a constraint to an adaptive algorithm improves its performance properties. Consequently, in this work, a noise-constrained least mean fourth (NCLMF) adaptive algorithm is developed. The NCLMF algorithm is based on a constrained minimization problem that includes knowledge of the noise variance. Moreover, this noise constrained LMF algorithm can be seen as a variable-step-size LMF algorithm. The convergence analysis as well the tracking analysis of the NCLMF adaptive algorithm are developed using the concept of energy conservation. Finally, simulation results are presented to demonstrate the superiority of the NCLMF algorithm over the conventional LMF algorithm as well corroborating the theoretical findings.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3706-3709
Number of pages4
ISBN (Print)9781424442966
DOIs
StatePublished - 2010

Publication series

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

Keywords

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

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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