We introduce an intelligent cooperative control system for ground target tracking in a cluttered urban environment with a team of Unmanned Air Vehicles (UAVs). We extend the work of Yu et. al. [1] to add a machine learning component that uses observations of target position to learn a model of target motion. Our learner is the Sequence Memoizer [2], a Bayesian model for discrete sequence data, which we use to predict future target location identifiers, given a context of previous location identifiers. Simulated cooperative control of a team of 3 UAVs in a 100-block city filled with various sizes of buildings verifies that learning a model of target motion can improve target tracking performance.


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    Titel :

    Intelligent cooperative control for urban tracking with Unmanned Air Vehicles


    Beteiligte:
    Cook, Kevin (Autor:in) / Bryan, Everett (Autor:in) / Yu, Huili (Autor:in) / Bai, He (Autor:in) / Seppi, Kevin (Autor:in) / Beard, Randal (Autor:in)


    Erscheinungsdatum :

    2013-05-01


    Format / Umfang :

    418298 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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