UWB LOS/NLOS Identification Using Spatio-Temporal Deep Learning

作者：Luchao Qi, Yun Zhao, Qiang Guo, Mykola Kaliuzhnyi · 发表于：IEEE Communications Letters · 年份：2025 · DOI：10.1109/lcomm.2025.3640700 · 被引用次数：2 · 研究领域：Indoor and Outdoor Localization Technologies、Ultra-Wideband Communications Technology、Microwave Imaging and Scattering Analysis

Reliable identification of line-of-sight (LOS) and non-line-of-sight (NLOS) conditions is critical for accurate localization in ultra-wideband (UWB) systems. In this letter, we propose a Voronoi+CNN-BiLSTM architecture for LOS/NLOS classification of UWB signals. By integrating Voronoi-based spatial propagation features, the model captures geometric characteristics of the environment to enhance classification performance. Experimental results on the publicly available UPT dataset demonstrate that the proposed method achieves an overall accuracy of 97.43%. Cross-scene evaluations demonstrate robust generalization. Moreover, the model achieves an average inference time of 2.08 ms per sample on a standard CPU, making it suitable for real-time applications.