Feature Map Aided Robust High Precision GNSS Positioning in Harsh Urban Environments

authored by: Fabian Ruwisch, Steffen Schön
Abstract: In this contribution, we propose the GNSS Feature Map-aided robust extended Kalman filter, which can provide centimeter-to-decimeter-level GNSS RTK position accuracy in urban environments without the need of additional sensors, city model information or computational intensive ray tracing methods. In this approach, the information on the predicted observation error magnitudes from the generated GNSS Feature Map is combined with the concept of robust estimation. The RTK positioning performance comparison for a dynamic experiment under harsh signal propagation conditions reveals that GNSS Feature Map-aided weighting using the Geman-McClure loss function shows the best overall performance. The RMS of the horizontal position error is improved by 17 % compared to C/N0 weighting, while 3DMA NLOS exclusion even degrades the solution. Furthermore, the combination of Feature Map information with the robust Geman-McClure loss function is effectively enhancing the float solution and reducing the number of falsely fixed ambiguities.
Organisation(s): Institute of Geodesy
Type: Article
Journal: IEEE Transactions on Intelligent Transportation Systems
ISSN: 1524-9050
Publication date: 26.05.2025
Publication status: E-pub ahead of print
Peer reviewed: Yes
ASJC Scopus subject areas: Automotive Engineering, Mechanical Engineering, Computer Science Applications
Electronic version(s): https://doi.org/10.1109/TITS.2025.3569975 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{191d47b78b074157a564df12397995a3,
title = "Feature Map Aided Robust High Precision GNSS Positioning in Harsh Urban Environments",
abstract = "In this contribution, we propose the GNSS Feature Map-aided robust extended Kalman filter, which can provide centimeter-to-decimeter-level GNSS RTK position accuracy in urban environments without the need of additional sensors, city model information or computational intensive ray tracing methods. In this approach, the information on the predicted observation error magnitudes from the generated GNSS Feature Map is combined with the concept of robust estimation. The RTK positioning performance comparison for a dynamic experiment under harsh signal propagation conditions reveals that GNSS Feature Map-aided weighting using the Geman-McClure loss function shows the best overall performance. The RMS of the horizontal position error is improved by 17 % compared to C/N0 weighting, while 3DMA NLOS exclusion even degrades the solution. Furthermore, the combination of Feature Map information with the robust Geman-McClure loss function is effectively enhancing the float solution and reducing the number of falsely fixed ambiguities.",
keywords = "GNSS feature map, Robust statistics, RTK, urban GNSS",
author = "Fabian Ruwisch and Steffen Sch{\"o}n",
note = "Publisher Copyright: {\textcopyright} 2000-2011 IEEE.",
year = "2025",
month = may,
day = "26",
doi = "10.1109/TITS.2025.3569975",
language = "English",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}