On sequential classification of autoregression processes with unknown noise variance

A. A. Dmitrienko, V. V. Konev

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    3 Citations (Scopus)

    Abstract

    The problem of guaranteed distinguishing of a finite hypothesis number in regard to an autoregression process by direct and indirect observations (with additive noise) is considered. For every hypothesis the autoregression parameters are assumed to be known and variances of a noise process and interferences in the observation channel may take arbitrary values. Sequential classification procedures with guaranteed probability of correct solution are suggested. Asymptotic formulas are obtained for procedure duration.

    Original languageEnglish
    Pages (from-to)51-62
    Number of pages12
    JournalProblemy Peredachi Informatsii
    Volume31
    Issue number4
    Publication statusPublished - Oct 1995

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    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

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