Revisiting adaptive least-squares estimation and application to online sparse signal recovery

Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis

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

4 Scopus citations

Abstract

This paper presents a novel time-adaptive estimation technique by revisiting the classical Wiener-Hopf equation. Any convex and not necessarily differentiable function can be used for enlarging the Wiener-Hopf equation in order to incorporate the often met, in practice, measurement and model inaccuracies. Unlike classical techniques, e.g., the Recursive Least Squares (RLS) algorithm, the proposed method is free of the computation of the inverse of a correlation matrix. Moreover, the method offers the means for dealing with the presence of convex constraints in an efficient way, by exploiting general convex analytic tools. To validate the proposed estimation method, an application of increasing importance nowadays, the online sparse signal recovery task is considered. Numerical results support the introduced theoretical arguments against the sparsity-aware classical batch, and the very recently introduced RLS-based signal recovery techniques.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages4292-4295
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

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

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • Least-squares estimation
  • Wiener-Hopf equation
  • adaptive filtering
  • projection
  • sparsity
  • subgradient

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