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Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning
March 29, 2024, 4:42 a.m. | Dieter Coppens, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract accuracy anchors arxiv collection cs.lg data data collection eess.sp effects error error correction errors however line reinforcement reinforcement learning tags technology truth type
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