In this paper, we study the localization of ground users by utilizing unmanned aerial vehicles (UAVs) as aerial anchors. Specifically, we introduce a novel localization framework based on Federated Learning (FL) and Reinforcement Learning (RL). In contrast to the existing literature, our scenario includes multiple UAVs learning the trajectory in different environment settings which results in faster convergence of RL model for minimum localization error. Furthermore, to evaluate the learned trajectory from the aggregated model, we test the trained RL agent in an alternative environment which shows the improvement over the localization error and convergence speed. Simulation results show that our proposed framework outperforms a model trained with transfer learning by .
Federated Reinforcement Learning UAV Trajectory Design for Fast Localization of Ground Users
2022-08-01
Shahbazi , A , Donevski , I , Nielsen , J J & Di Renzo , M 2022 , Federated Reinforcement Learning UAV Trajectory Design for Fast Localization of Ground Users . in 2022 30th European Signal Processing Conference (EUSIPCO) . , 9909614 , IEEE , European Signal Processing Conference , vol. 2022-August , pp. 663-666 , 30th European Signal Processing Conference, EUSIPCO 2022 , Belgrade , Serbia , 29/08/2022 . https://doi.org/10.23919/EUSIPCO55093.2022.9909614
Article (Journal)
Electronic Resource
English
DDC: | 629 |
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BASE | 2021
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