This work combines the physical, kinematic, and statistical properties of targets, clutter, and sensor calibration as manifested in multichannel synthetic aperture radar (SAR) imagery into a unified Bayesian structure that simultaneously estimates 1) clutter distributions and nuisance parameters, and 2) target signatures required for detection/inference. A Monte Carlo estimate of the posterior distribution is provided that infers the model parameters directly from the data with little tuning of algorithm parameters. Performance is demonstrated on both measured/synthetic wide-area datasets.


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    Titel :

    Moving target inference with bayesian models in SAR imagery


    Beteiligte:
    Newstadt, Gregory (Autor:in) / Zelnio, Edmund (Autor:in) / Hero, Alfred (Autor:in)


    Erscheinungsdatum :

    01.07.2014


    Format / Umfang :

    1758072 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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