Abstract
The sensor resource management problem in a multi-object tracking scenario is considered. In order to solve it, a dynamic budget balancing algorithm is proposed which models the different sensor tasks as partially observable Markov decision processes. Those are being solved by applying a combination of Lagrangian relaxation and policy rollout. The algorithm converges to a solution which is close to the optimal steady-state solution. This is shown through simulations of a two-dimensional tracking scenario. Moreover, it is demonstrated how the algorithm allocates the sensor time budgets dynamically to a changing environment and takes predictions of the future situation into account.
| Original language | English |
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| Title of host publication | 2020 23rd International Conference on Information Fusion (FUSION) |
| Subtitle of host publication | Proceedings |
| Publisher | IEEE |
| Pages | 1-8 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-0-578-64709-8 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | 23rd International Conference on Information Fusion (FUSION 2020) - Virtual, South Africa Duration: 6 Jul 2020 → 9 Jul 2020 https://www.fusion2020.org/ |
Conference
| Conference | 23rd International Conference on Information Fusion (FUSION 2020) |
|---|---|
| Abbreviated title | FUSION |
| Country/Territory | South Africa |
| City | Virtual |
| Period | 6/07/20 → 9/07/20 |
| Internet address |
Keywords
- Lagrangian Relaxation
- Partially Observable Markov Decision Process
- Policy Rollout
- Sensor Resource Management