Machine learning based multipath mitigation for high-precision GNSS data processing

Abstract

Multipath is the main unmodeled error source hindering high-precision GNSS (Global Navigation Satellite System) data processing. Classical multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: they are either too complicated for implementation or not effective enough for multipath mitigation. In this study, we demonstrate that machine learning (ML) based models, such as random forest, can overcome these drawbacks by spatial interpolation over sky map and thus mitigate multipath effectively. 30 days of 1 Hz geodetic grade GPS data as well as 6 days of low-cost data are used to train and test the ML models. Based on a series of test cases, the best number of days for model training and the validity period for the models are discussed in this contribution. For quantification, the multipath reduction rate and kinematic positioning precision are computed using different ML models and compared to those derived from SF and MHM. The statistical results show that the XGBoost ML model can achieve higher multipath reduction rates compared to SF and MHM, especially for pseudorange measurements, which is important for low-cost devices. It reduces the multipath by 48% and 55% for pseudorange and carrier phase measurements, respectively, and outperforms SF (40% and 52%) and MHM (37% and 49%). The positioning precision when using different multipath models is similar, with differences of less than 1 mm. We conclude that the ML based multipath mitigation method is effective and easy-to-use, which can be applied under real-time scenarios.