Removing the Need for Ground Truth UWB Data Collection: Self-Supervised Ranging Error Correction Using Deep Reinforcement Learning

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 labe...

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Bibliographic Details
Main Authors: Dieter Coppens, Ben van Herbruggen, Adnan Shahid, Eli de Poorter
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
Indoor positioning
UWB
reinforcement learning
self-supervised
error-correction
Online Access:https://ieeexplore.ieee.org/document/10695458/
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https://ieeexplore.ieee.org/document/10695458/

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