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.

翻译：基于超宽带（UWB）测量的定位系统在复杂环境中，由于存在非视距（NLOS）误差，可能导致性能不佳。基于学习的误差缓解方法通过直接利用宽带波形而非人工提取特征，表现出显著的性能提升。然而，这些方法需要完全标注实际测量误差的数据样本进行训练，导致数据采集耗时。本文提出一种基于变分贝叶斯的半监督学习方法，用于UWB测距误差缓解。该方法结合深度学习技术与统计工具，能够高效地从标注和未标注数据样本中积累知识。大量实验验证了该方法在不同监督率下的有效性，并且即使在低监督率下，其性能也优于其他全监督方法。