Fabian Ruwisch and Steffen Schön, Institut für Erdmessung, Leibniz Universität Hannover, Germany

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Urban environments are challenging for GNSS (Global Navigation Satellite System) signal propagation. Surrounding buildings cause signal reflection and blockage resulting in numerous non-line-of-sight (NLOS) and multipath (LOS plus reflection) signal receptions. In a kinematic application, like e.g., pedestrian navigation and autonomous driving, the main error sources in urban areas (NLOS biases, multipath) have a complex spatiotemporal behaviour, i.e. their occurrence and magnitude depend on the satellite ray direction, the user antenna location and properties as well as on the buildings in the surrounding area. In this study, we present a new method for mapping GNSS signal propagation-related features, which depend on both the varying user antenna location and satellite position, into one common GNSS Feature Map. Our proposed approach improves the understanding of GNSS signal propagation in challenging environments, especially for kinematic trajectories in urban trenches. Based on this map, users can identify environmental structures and critical trajectory sections for both the user locations and satellite positions and thus exploit the map for trajectory planning. Furthermore, we show how the GNSS Feature Map can contribute to the correction of NLOS biases. Ray tracing can be performed offline at given positions in the 3D model which provides the predicted errors to users that will pass at that location simultaneously minimizing the computation load at the rover. Our proposed approach of NLOS bias correction reduces the observed DD biases due to NLOS reception by 50% for the 68%-quantile. Thereby the distance to the reference way point of the map is not important if we assume a search space of 5 m around the ground truth to make sure we are comparing positions in the same street.