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Ultra-wideband (UWB)-based techniques, while becoming mainstream approachesfor high-accurate positioning, tend to be challenged by ranging bias in harshenvironments. The emerging learning-based methods for error mitigation haveshown great performance improvement via exploiting high semantic features fromraw data. However, these methods rely heavily on fully labeled data, leading toa high cost for data acquisition. We present a learning framework based on weaksupervision for UWB ranging error mitigation. Specifically, we propose a deeplearning method based on the generalized expectation-maximization (GEM)algorithm for robust UWB ranging error mitigation under weak supervision. Suchmethod integrate probabilistic modeling into the deep learning scheme, andadopt weakly supervised labels as prior information. Extensive experiments invarious supervision scenarios illustrate the superiority of the proposedmethod.

Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation