Tight Integration of GNSS and a 3D City Model for Robust Positioning in Urban Canyons

Positioning and navigation by GNSS in urban context are always challenging tasks, because of signal propagation problems such as shadowing effects and multipath. When not enough GNSS signals are received in line-of-sight (LOS), classical approaches mitigating multipath effects become insufficient because there is not enough reliable information available. Consequently, positioning errors can be about tens of meters, especially in urban canyons. In this paper, we introduce a GNSS positioning approach that uses constructively non-line-of-sight (NLOS) signals in order to have enough information to compute the user's position. In this work, we use the SE-NAV software to predict the geometric paths of NLOS signals using a high realistic 3D model of the environment. More precisely, we propose a new version of the extended Kalman filter augmented by the information provided by SE-NAV, referred to as 3D AEKF, for GNSS navigation in NLOS context. In the proposed approach, the measurement model traditionally based on the trilateration equations is constructed from the received paths estimated by SENAV. The Jacobian of the measurement model is calculated through knowledge of the objects on which the reflections have occured. To use even less reliable measurements, we propose a robust version of the 3D AEKF. Simulations conducted in realistic scenarios allow the performance of the proposed method to be evaluated.