Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management
<|reference_start|>Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management: 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.<|reference_end|>
@article{jones2024non-myopic,
    title={Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management},
    author={George Jones, Angel Garcia-Fernandez, Christian Blackman},
    journal={arXiv preprint arXiv:2405.05815},
    year={2024},
    doi={10.1109/TAES.2024.3418750},
    archivePrefix={arXiv},
    eprint={2405.05815},
    primaryClass={eess.SY cs.SY}
}