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.
Nonmyopic GOSPA-Driven Gaussian Bernoulli Sensor Management
IEEE Transactions on Aerospace and Electronic Systems ; 60 , 6 ; 7628-7642
01.12.2024
1543225 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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