Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning

• URL: http://arxiv.org/abs/2403.19262v2
• Date: Tue, 01 Oct 2024 08:05:23 GMT
• Title: Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning
• Authors: Dieter Coppens, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter,
• Abstract summary: 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. This paper proposes a novel self-supervised deep reinforcement learning approach that does not require labeled ground truth data.
• Score: 1.4061979259370274
• License:
• 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 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.

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