An efficient parallel variable-metric projection algorithm based on set-theoretic and Krylov-proportionate adaptive filtering techniques

Masahiro Yukawa, Isao Yamada

研究成果: Paper査読

抄録

We blend two adaptive filtering techniques for further efficiency: the set-theoretic adaptive filter (STAF, Yamada et al. 2002) and the Krylov-proportionate adaptive filter (KPAF, Yukawa 2009). Although the ideas behind these techniques are quite different from each other, there is a way to blend them together by noticing that KPAF can be seen as a sort of 'variable-metric' projection algorithm. We propose a blended algorithm named set-theoretic Krylov-proportionate adaptive filter (SKAF), which features iterative parallel variable-metric projection onto well-designed closed convex sets. We present comparisons in complexity and mean square error (MSE) performance, showing significant advantages of the proposed algorithm over the existing algorithms.

本文言語English
ページ418-421
ページ数4
出版ステータスPublished - 2009 12 1
外部発表はい
イベントAsia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009 - Sapporo, Japan
継続期間: 2009 10 42009 10 7

Other

OtherAsia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009
国/地域Japan
CitySapporo
Period09/10/409/10/7

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 電子工学および電気工学
  • 通信

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