Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning
<|reference_start|>Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning: Indoor positioning using UWB technology has gained interest due to its centimeter-level accuracy potential. However, multipath effects and non-line-of-sight conditions cause ranging errors between anchors and tags. Existing approaches for mitigating these ranging errors rely on collecting large labeled datasets, making them impractical for real-world deployments. This paper proposes a novel self-supervised deep reinforcement learning approach that does not require labeled ground truth data. A reinforcement learning agent uses the channel impulse response as a state and predicts corrections to minimize the error between corrected and estimated ranges. The agent learns, self-supervised, by iteratively improving corrections that are generated by combining the predictability of trajectories with filtering and smoothening. Experiments on real-world UWB measurements demonstrate comparable performance to state-of-the-art supervised methods, overcoming data dependency and lack of generalizability limitations. This makes self-supervised deep reinforcement learning a promising solution for practical and scalable UWB-ranging error correction.<|reference_end|>
@article{coppens2024removing,
    title={Removing the need for ground truth UWB data collection: self-supervised
  ranging error correction using deep reinforcement learning},
    author={Dieter Coppens, Ben Van Herbruggen, Adnan Shahid and Eli De Poorter},
    journal={arXiv preprint arXiv:2403.19262},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19262},
    primaryClass={eess.SP cs.LG}
}