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Géolocalisation et navigation

28-29 mars, 2018
Observatoire de Paris

Site de Meudon

Rechercher > Par intervenant > Zhu Ni

GNSS Integrity Enhancement for urban Transport Applications by Error Characterization and Fault Detection and Exclusion (FDE)
Ni Zhu  1, *@  , David Bétaille  2@  , Juliette Marais  1@  , Marion Berbineau  3@  
1 : Laboratoire Électronique Ondes et Signaux pour les Transports  (IFSTTAR/COSYS/LEOST)  -  Site web
Institut français des sciences et technologies des transports, de l'aménagement et des réseaux, PRES Université Lille Nord de France
20 rue Elisée Reclus, BP317, 59666 Villeneuve dÁscq Cedex -  France
2 : L'Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux  (IFSTTAR)  -  Site web
IFSTTAR-COSYS
Route de la Bouaye, 44340 Bouguenais -  France
3 : L'Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux  (IFSTTAR)  -  Site web
IFSTTAR-COSYS
20 rue Elisée Reclus, BP317, 59666 Villeneuve dÁscq Cedex -  France
* : Auteur correspondant

In the past decades, more and more Global Navigation Satellite Systems (GNSS)-based urban transport applications emerged. Among these applications, the liability critical ones, such as Electronic Toll Collection (ETC) and Pay as you Drive insurance, have high requirements for positioning accuracy as well as integrity since large errors can lead to serious consequences. Yet urban environments present great challenges for GNSS positioning due to the existence of multipath effects and None-Line-of-Sight (NLOS) receptions. This article presents a complete integrity monitoring scheme for urban transport applications. This scheme is realized in several levels. Firstly, measurement errors are better characterized by using weighting models with the help of an Urban Multipath Modeling (UMM). Secondly, several Fault Detection and Exclusion (FDE) methods are applied in order to detect and exclude erroneous measurements. Finally, Horizontal Protection Levels (HPLs) are computed and the probability of Misleading Information (MI) is analyzed.


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