This work addresses the task of active sensing, or information-seeking control of mobile sensor platforms. Formulation of a control objective in terms of information gain allows mobile sensors to be both autonomous and easily reconfigurable to include a variety of sensor and target models. Tracking a moving target using a camera mounted on a fixed-wing unmanned aircraft is considered, but the control formulation is not specific to this choice of sensor or estimation task. A control formulation is developed which minimizes the entropy of an estimate distribution over a receding horizon subject to stochastic non-linear models for both the target motion and sensors. Previous similar work has been restricted to either a stationary target, a horizon of length one, or Gaussian estimates. The prediction of conditional entropy is shown to be inherently complex, and a computationally efficient sequential Monte Carlo method is developed. The entropy prediction depends on this Monte Carlo method as well as a novel approach for entropy calculation in the context of particle filtering. These methods are verified through simulation and post-processing of experimental flight data.


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    Titel :

    Particle filter based information-theoretic active sensing


    Weitere Titelangaben:

    Auf Teilchenfilter basierende informationstheoretische aktive Sensorerfassung


    Beteiligte:
    Ryan, Allison (Autor:in) / Hedrick, J.Karl (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2010


    Format / Umfang :

    11 Seiten, 13 Bilder, 2 Tabellen, 27 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch





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