Sparsification of Input signal for Fixed Order Implementation of KPNLMS Adaptive Filters

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  • KOGA Masakazu
    Department of Information and Communications Systems Engineering, Tokyo Metropolitan University
  • MARU Yuji
    Department of Information and Communications Systems Engineering, Tokyo Metropolitan University
  • NISHIKAWA Kiyoshi
    Department of Information and Communications Systems Engineering, Tokyo Metropolitan University

Bibliographic Information

Other Title
  • 固定次数でのKPNLMS適応フィルタの実現のためのスパース化手法(画像・メディア処理技術,および一般)
  • 固定次数でのKPNLMS適応フィルタの実現のためのスパース化手法
  • コテイ ジスウ デ ノ KPNLMS テキオウ フィルタ ノ ジツゲン ノ タメ ノ スパースカ シュホウ

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Abstract

A kernel adaptive algorithm enables to learn a nonlinear system by applying kernel method. However, increasing the number of training vectors while estimation processes causes a huge computational cost. This paper proposes a sparsification of proportionate-type of KNLMS, which updates filter coefficients with the individual step size, in the case that the size of the dictionary is limited.

Journal

  • ITE Technical Report

    ITE Technical Report 37.56 (0), 61-64, 2013

    The Institute of Image Information and Television Engineers

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