A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
Date
2021-12-30Metadata
Show full item recordAbstract
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 semisupervised
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.