A class of adaptively regularised PNLMS algorithms

Beth Jelfs, Danilo P. Mandic, Jacob Benesty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

A class of algorithms representing a robust variant of the proportionate normalised least-mean-square (PNLMS) algorithm is proposed. To achieve this, adaptive regularisation is introduced within the PNLMS update, with the analysis conducted for both individual and global regularisation factors. The update of the adaptive regularisation parameter is also made robust, to improve steady state performance and reduce computational complexity. The proposed algorithms are better suited not only for operation in nonstationary environments, but are also less sensitive to changes in the input dynamics and the choice of their parameters. Simulations in a sparse environment show the proposed class of algorithms offer enhanced performance and increased stability over the standard PNLMS.

Original languageEnglish
Title of host publication2007 15th International Conference on Digital Signal Processing, DSP 2007
Pages19-22
Number of pages4
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 15th International Conference onDigital Signal Processing, DSP 2007 - Wales, United Kingdom
Duration: 1 Jul 20074 Jul 2007

Publication series

Name2007 15th International Conference on Digital Signal Processing, DSP 2007

Conference

Conference2007 15th International Conference onDigital Signal Processing, DSP 2007
Country/TerritoryUnited Kingdom
CityWales
Period1/07/074/07/07

Keywords

  • Adaptive regularisation
  • LMS
  • Normalised LMS (NLMS)
  • Proportionate NLMS (PNLMS)

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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