  Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches
for high-accurate positioning, tend to be challenged by ranging bias in harsh
environments. The emerging learning-based methods for error mitigation have
shown great performance improvement via exploiting high semantic features from
raw data. However, these methods rely heavily on fully labeled data, leading to
a high cost for data acquisition. We present a learning framework based on weak
supervision for UWB ranging error mitigation. Specifically, we propose a deep
learning method based on the generalized expectation-maximization (GEM)
algorithm for robust UWB ranging error mitigation under weak supervision. Such
method integrate probabilistic modeling into the deep learning scheme, and
adopt weakly supervised labels as prior information. Extensive experiments in
various supervision scenarios illustrate the superiority of the proposed
method.
