Localization systems based on ultra-wide band (UWB) measurements can have
unsatisfactory performance in harsh environments due to the presence of
non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation
have shown great performance improvement via directly exploiting the wideband
waveform instead of handcrafted features. However, these methods require data
samples fully labeled with actual measurement errors for training, which leads
to time-consuming data collection. In this paper, we propose a semi-supervised
learning method based on variational Bayes for UWB ranging error mitigation.
Combining deep learning techniques and statistic tools, our method can
efficiently accumulate knowledge from both labeled and unlabeled data samples.
Extensive experiments illustrate the effectiveness of the proposed method under
different supervision rates, and the superiority compared to other fully
supervised methods even at a low supervision rate.

本文提出了一种基于变分贝叶斯的半监督深度学习方法，用于 UWB 定位系统的误差校正，通过融合深度学习技术和统计工具，实现从有标记和未标记的数据样本中高效地累积知识，相较于其他全监督方法，即使在低监督比例下，也取得了更好的性能。