Ray Tracing-Based Localization Method for 5G Signals in LOS/NLOS Hybrid Scenarios

Haoxuan Yang, Chao Sun, Yingzhe He, Lu Bai, Jiayue Lei, Xiaowei Jin, Ying Xu

Abstract:	High-precision positioning is increasingly critical in autonomous driving, low-altitude economy, and smart manufacturing, yet traditional global navigation satellite systems (GNSS) face severe performance degradation in urban areas due to signal blockage and multipath effects. With the advent of 5G, techniques such as millimeter-wave transmission, multiple-input multipleoutput (MIMO), and hybrid observables create new opportunities for high-precision positioning. However, non-line-of-sight (NLOS) propagation remains a major challenge, as distorted time-based and angle-based measurements significantly reduce accuracy. This paper presents a ray tracing-based 5G localization method designed for hybrid line-of-sight (LOS)/NLOS scenarios. By incorporating map information and signal state thresholds, the approach effectively eliminates invalid candidate points while maintaining computational efficiency. Compared with traditional ray tracing-based localization method, it demonstrates improved adaptability in complex urban environments. Simulation studies under diverse conditions confirm its capability to enhance positioning accuracy and reliability, showing strong potential for next-generation intelligent positioning services beyond traditional GNSS solutions.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	2702 - 2713
Cite this article:	Yang, Haoxuan, Sun, Chao, He, Yingzhe, Bai, Lu, Lei, Jiayue, Jin, Xiaowei, Xu, Ying, "Ray Tracing-Based Localization Method for 5G Signals in LOS/NLOS Hybrid Scenarios," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 2702-2713. https://doi.org/10.33012/2025.20436
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