Optimizing LOS/NLOS Modeling and Solution Determination for 3D-Mapping-Aided GNSS Positioning

Qiming Zhong, Paul Groves

Abstract:	In urban environments, the propagation of satellite signals may be affected by buildings, resulting in poor performance of conventional GNSS positioning. Several studies have shown that 3D mapping data of buildings significantly improves GNSS positioning by predicting which signals are line-of-sight (LOS) and which are non-line-of-sight (NLOS). This study introduces several improvements to current UCL’s 3DMA GNSS techniques, including enhanced satellite visibility prediction for overhanging structures, inclusion of untracked satellites for shadow matching to improve satellite geometry, Bayesian inference-based shadow matching adaptable to various densities of urban environments, a new NLOS model for likelihood-based ranging, and a region growing-based clustering algorithm to manage ambiguity. The effectiveness of these enhancements was validated using GNSS datasets collected in London, representing diverse urban scenarios. The results show that the enhanced 3DMA GNSS algorithm improves the RMS position error in the horizontal radial direction by more than 20% compared to the original version.
Published in:	Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023) September 11 - 15, 2023 Hyatt Regency Denver Denver, Colorado
Pages:	373 - 402
Cite this article:	Zhong, Qiming, Groves, Paul, "Optimizing LOS/NLOS Modeling and Solution Determination for 3D-Mapping-Aided GNSS Positioning," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 373-402. https://doi.org/10.33012/2023.19406
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