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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.05815 |
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| _version_ | 1866929401060392960 |
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| author | Jones, George Garcia-Fernandez, Angel Blackman, Christian |
| author_facet | Jones, George Garcia-Fernandez, Angel Blackman, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05815 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management Jones, George Garcia-Fernandez, Angel Blackman, Christian Systems and Control 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. |
| title | Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2405.05815 |