Abstract
In this paper we propose a Bayesian agent network planning algorithm for information-criterion-based measurement selection for cooperative localization in static networks with anchors. This allows to increase the accuracy of the agent positioning while keeping the number of measurements between agents to a minimum. The proposed algorithm is based on minimizing the conditional differential entropy (CDE) of all agent states to determine the optimal set of measurements between agents. Such combinatorial optimization problems have factorial runtime and quickly become infeasible, even for a rather small number of agents. Therefore, we propose a Bayesian agent network planning algorithm that performs a local optimization for each state. Experimental results demonstrate a performance improvement compared to a random measurement selection strategy, significantly reducing the position RMSE at a smaller number of measurements between agents.
| Original language | English |
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| Title of host publication | 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings |
| Place of Publication | Dublin, Ireland |
| Pages | 1-7 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781728174402 |
| DOIs | |
| Publication status | Published - Jun 2020 |
| Event | 2020 IEEE International Conference on Communications: ICC Workshops 2020 - Convention Centre Dublin, Virtuell, Ireland Duration: 7 Jun 2020 → 11 Jun 2020 |
Conference
| Conference | 2020 IEEE International Conference on Communications |
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| Abbreviated title | IEEE ICC 2020 |
| Country/Territory | Ireland |
| City | Virtuell |
| Period | 7/06/20 → 11/06/20 |
ASJC Scopus subject areas
- Information Systems and Management
- Artificial Intelligence
- Control and Optimization
- Signal Processing
- Computer Networks and Communications