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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    Title :

    Moving target inference with bayesian models in SAR imagery


    Contributors:


    Publication date :

    2014-07-01


    Size :

    1758072 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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