A new modeling based on urban trenches to improve GNSS positioning Quality of Service in cities

Digital maps with 3D data proved to make it possible the determination of Non-Line-Of-Sight (NLOS) satellites in real time, whilst moving, and obtain significant benefit in terms of navigation accuracy. However, such data are difficult to handle with Geographical Information System (GIS) embedded software in real time. The idea developed in this article consists is proposing a method, light in terms of information contents and computation throughput, for taking into account the knowledge of the 3D environment of a vehicle in a city, where multipath phenomena can cause severe errors in positioning solution. This method makes use of a digital map where homogeneous sections of streets have been identified, and classified among different types of urban trenches. This classification is so called: “Urban Trench Model”. Not only NLOS satellites can be detected, but also, if needed, the corresponding measurements can be corrected and further used in the positioning solver. The paper presents in details the method and its results on several real test sites, with a demonstration of the gain obtained on the final position accuracy. The benefit of the Urban Trench Model, i.e. the reduction of positioning errors as compared to conventional solver considering all satellites, gets up to an amount between 30% and as much as 70% e.g. in Paris.

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Additional Info

Field Value
Source ISSN: 1939-1390
Author Betaille, David, Peyret, François, Ortiz, Miguel, Miquel, Stéphan, Fontenay, Leila
Maintainer CCSD
Last Updated May 7, 2026, 21:46 (UTC)
Created May 7, 2026, 21:46 (UTC)
Identifier hal-00915788
Language en
contributor Géolocalisation (IFSTTAR/COSYS/GEOLOC) ; Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Nantes Angers Le Mans (UNAM)
creator Betaille, David
date 2013-01-01T00:00:00
harvest_object_id ad11be4e-162b-4c3e-87fd-0017d79f882b
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2023-08-07T00:00:00
relation info:eu-repo/semantics/altIdentifier/doi/10.1109/MITS.2013.2263460
set_spec type:ART