In the Internet of Things (IoT), ultra-wideband (UWB) plays an essential role in localization and navigation. However, in indoor environments, UWB signals are often blocked by obstacles, leading to non-line-of-sight (NLOS) propagation. Thus, reliable line-of-sight (LOS)/NLOS identification is essential for reducing errors and enhancing the robustness of localization. This paper focuses on a single-anchor UWB configuration and proposes a temporal deep learning framework that jointly exploits two-way ranging (TWR) and angle-of-arrival (AOA) measurements for LOS/NLOS identification. At the core of the model is a temporal convolutional network (TCN) augmented with a self-attentive pooling mechanism, which enables the extraction of dynamic propagation patterns and temporal contextual information. Experimental evaluations on real-world measurement data show that the proposed method achieves an accuracy of 96.65% on the collected dataset and yields accuracies ranging from 88.72% to 93.56% across the three scenes, outperforming representative deep learning baselines. These results indicate that jointly exploiting geometric and temporal information in a single-anchor configuration is an effective approach for robust UWB indoor positioning.
Zeng et al. (Tue,) studied this question.