Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management
arXiv:2405.05815 · doi:10.1109/TAES.2024.3418750
Abstract
In this paper, we propose an algorithm for non-myopic 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 minimisation problem, whose cost function is the mean square generalised optimal sub-pattern 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.
arXiv commentPaper accepted to IEEE Transactions on Aerospace and Electronic Systems, 25th June 2024
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