Fast adaptive algorithms using eigenspace projections

N. Gopalan Nair, Andreas Spanias

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Although adaptive gradient algorithms are relatively robust, they generally have poor performance in the absence of "rich" excitation. It is well known that the convergence speed of the LMS algorithm deteriorates when the condition number of the autocorrelation matrix of the input is large. This problem has been addressed using RLS, Weighted RLS (WRLS), as well as normalized frequency-domain algorithms. In this paper, we present an alternative approach that employs gradient projections in selected eigenvector subspaces to improve the convergence properties of LMS algorithms. We also use an auxiliary algorithm that iteratively updates selected eigensubspaces. The proposed algorithm is efficient in terms of complexity and its convergence speed approaches that of the WRLS for a certain class of excitation signals.

Original languageEnglish (US)
Pages (from-to)1929-1935
Number of pages7
JournalSignal Processing
Volume83
Issue number9
DOIs
StatePublished - Sep 1 2003

Keywords

  • Adaptive algorithms
  • Eigenspace estimation
  • Gradient projections

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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