Several forms of sequential hypothesis testing algorithms are described and their performance as classification algorithms for automatic target recognition is evaluated and compared. Several forms of parameteric algorithms, as well as a sequential form of a useful nonparametric algorithm are considered. The primary focus is the design of algorithms for automatic target recognition that produce maximally reliable decisions while requiring, on the average, a minimum number of backscatter measurements. The tradeoffs between the average number of required measurements and the error performance of the resulting algorithms are compared by means of Monte-Carlo simulation studies.<>


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    Titel :

    M-ary sequential hypothesis tests for automatic target recognition


    Beteiligte:
    Jouny, I. (Autor:in) / Garber, F.D. (Autor:in)


    Erscheinungsdatum :

    01.04.1992


    Format / Umfang :

    875225 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch