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.

翻译：基于超宽带（UWB）的技术虽已成为高精度定位的主流方法，但在恶劣环境中仍面临测距偏差的挑战。新兴的基于学习的误差抑制方法通过从原始数据中提取高级语义特征，展现出显著的性能提升。然而，这些方法高度依赖完全标注数据，导致数据采集成本高昂。本文提出一种基于弱监督学习的UWB测距误差抑制框架。具体而言，我们提出一种基于广义期望最大化（GEM）算法的深度学习方法，用于在弱监督条件下实现鲁棒的UWB测距误差抑制。该方法将概率建模融入深度学习框架，并将弱监督标签作为先验信息。在多种监督场景下的大量实验表明，所提方法具有优越性。