A deterministic analysis of linearly constrained adaptive filtering algorithms

Masahiro Yukawa, Isao Yamada

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

This paper presents a mathematically rigorous analysis of linearly constrained adaptive filtering algorithms based on the adaptive projected subgradient method. We provide the novel concept of constraint-embedding functions that enables to analyze certain classes of linearly constrained adaptive algorithms in a unified manner. Trajectories of the linearly constrained adaptive filters always lie in the affine constraint set, a translation of a closed subspace. Based on this fact, we translate all the points on the constraint set to its underlying subspace . which we regard as a Hilbert space . thereby making the analysis feasible. Derivations of the linearly constrained adaptive filtering algorithms are finally presented in connection with the analysis.

Original languageEnglish
Pages (from-to)131-135
Number of pages5
JournalEuropean Signal Processing Conference
Publication statusPublished - 2011 Dec 1
Externally publishedYes
Event19th European Signal Processing Conference, EUSIPCO 2011 - Barcelona, Spain
Duration: 2011 Aug 292011 Sep 2

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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