A Bayesian classification algorithm is presented for discriminating buried land mines from buried and surface clutter in ground penetrating radar (GPR) signals. This algorithm is based on multivariate normal (MVN) clustering, where feature vectors are used to identify populations (clusters) of mines and clutter objects. The features are extracted from two-dimensional images created from ground penetrating radar scans. MVN clustering is used to determine the number of clusters in the data and to create probability density models for target and clutter populations, producing the MVN clustering classifier (MVNCC). The Bayesian information criteria (BIC) are used to evaluate each model to determine the number of clusters in the data. An extension of the MVNCC allows the model to adapt to local clutter distributions by treating each of the MVN cluster components as a Poisson process and adaptively estimating the intensity parameters. The algorithm is developed using data collected by the mine hunter/killer close-in detector (MH/K CID) at prepared mine lanes. The mine hunter/killer is a prototype mine detecting and neutralizing vehicle developed for the US Army to clear roads of anti-tank mines.


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    Titel :

    Buried land mine detection using multivariate normal clustering


    Beteiligte:
    Duston, B.M. (Autor:in)


    Erscheinungsdatum :

    2001


    Format / Umfang :

    10 Seiten, 5 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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