A GNSS Multipath and NLoS Mitigation Method for Urban Scenarios Based on Particle Filtering

Global navigation satellite systems (GNSSs) are at the basis of many location services. However, in harsh environments such as urban canyons, the performance can be highly degraded due to lack of satellite visibility and complex reflection phenomena like multipath and Non-Line-of-Sight (NLoS). This work aims at exploiting the consistency of the information provided by GNSS receivers to detect and mitigate the effect of multipath and NLoS on the positioning solution. The proposed method extends the definition of innovation for the Particle Filter (PF), while also exploiting its native capability to handle more complex probability models of the errors. The use of multi-modal probability densities adds robustness to the filter in harsh conditions. The proposed method has been tested on real open-source datasets, showing considerable improvement in terms of position error compared to other state-of-the-art solutions based on the Extended Kalman Filter (EKF).