Convergence properties of affine projection and normalized data reusing methods

Robert A. Soni, Kyle A. Gallivan, W. Kenneth Jenkins

    Research output: Contribution to journalConference articlepeer-review

    3 Scopus citations

    Abstract

    The coloring of input sequences can significantly reduce the effective convergence rate of normalized least mean squares (LMS) adaptive filtering algorithms. Recently, there has been significant interest in affine projection adaptive filtering algorithms. These algorithms offer improved performance over traditional normalized LMS algorithms. They can achieve the performance of recursive least squares techniques at a lower computational cost. Unfortunately, these algorithms can greatly amplify measurement noise leading to higher overall misadjustment and poor tracking abilities. In this paper, the new forms of data reusing developed by the authors will be shown to be able to approximate the convergence performance of the affine projection methods without the large misadjustment. In addition, a comprehensive analysis of the steady-state statistical convergence properties of a broad class of data-reusing algorithms will be presented.

    Original languageEnglish (US)
    Pages (from-to)1166-1170
    Number of pages5
    JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
    Volume2
    StatePublished - 1998
    EventProceedings of the 1998 32nd Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
    Duration: Nov 1 1998Nov 4 1998

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Computer Networks and Communications

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