In this article, we propose an algorithm for nonmyopic sensor management for Bernoulli filtering, i.e., when there may be at most one target present in the scene. The algorithm is based on selecting the action that solves a Bellman-type minimization problem, whose cost function is the mean square generalized optimal subpattern assignment (GOSPA) error, over a future time window. We also propose an implementation of the sensor management algorithm based on an upper bound of the mean square GOSPA error and a Gaussian single-target posterior. Finally, we develop a Monte Carlo tree search algorithm to find an approximate optimal action within a given computational budget. The benefits of the proposed approach are demonstrated via simulations.


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    Title :

    Nonmyopic GOSPA-Driven Gaussian Bernoulli Sensor Management


    Contributors:


    Publication date :

    2024-12-01


    Size :

    1543225 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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