Geragersian, PatrickPetrunin, IvanGuo, WeisiGrech, Raphael2023-01-112023-01-112022-10-31Geragersian P, Petrunin I, Guo W, Grech R. (2022) Multipath detection from GNSS observables using gated recurrent unit. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), 18-22 September 2022, Portsmouth, Virginia, USA978-1-6654-8608-82155-7195https://doi.org/10.1109/DASC55683.2022.9925850https://dspace.lib.cranfield.ac.uk/handle/1826/18926One of the most used Position, Navigation, and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit Line-Of-Sight (LOS) and increase position ambiguity. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as the transportation of organs that are time-sensitive. Therefore, techniques to mitigate Non-Line-Of-Sight (NLOS) interference are required for improved positioning accuracy. This paper proposes a Gated Recurrent Unit-based (GRU) multipath detection algorithm that uses pseudorange, ephemerides, Doppler shift, Carrier-To-Noise Ratio (C/N0), and elevation data from each satellite to determine whether multipath is present. Signals from the satellite classified as multipath are then flagged and ignored for Position, Velocity, and Timing (PVT) calculations until they are deemed as LOS. The classification algorithm is developed and tested on Spirent GSS7000 to generate GNSS Radio Frequency (RF). OKTAL-SE Sim3D is used to simulate urban canyon environments in which signals propagate from the satellite to the receiver. RF signals are then transmitted to a Ublox F9P GNSS receiver that can receive GPS and GLONASS signals which are processed to output PVT information. The data collected is used to train the GRU to classify received signals as no multipath or multipath. From performance evaluation, GRU outperforms decision tree, K-Nearest Neighbor (KNN) classifiers, and Support Vector Machines (SVM). Furthermore, comparing GRU with SVM, a 50% increase in accuracy is observed with a 95% error of 0.85 m for GRU compared to 1.78 m for SVM.enAttribution-NonCommercial 4.0 InternationalmultipathGRUGNSSmachine learningConference paper978-1-6654-8607-1