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.

翻译：超宽带（UWB）技术凭借厘米级定位精度潜力在室内定位领域备受关注。然而，多径效应和非视距条件会导致基站与标签之间的测距误差。现有误差抑制方法依赖大规模标注数据集，难以在实际部署场景中应用。本文提出一种新型自监督深度强化学习方法，无需标注真实数据。强化学习智能体将信道冲激响应作为状态输入，通过预测修正量实现校正距离与估计距离的误差最小化。该智能体结合轨迹可预测性与滤波平滑技术，通过迭代优化生成的修正量实现自监督学习。基于真实UWB测量数据的实验表明，该方法在性能上与当前最先进的监督学习方法相当，同时克服了数据依赖性和泛化能力不足的局限。自监督深度强化学习为实用化、可扩展的UWB测距误差校正提供了可行方案。