Data driven Vertical Total Electron Content workflow for GNSS positioning for single frequency receivers
Global Navigation Satellite Systems (GNSS) systems are largely affected by ionospheric effects, which are larger in low latitudes. Traditional mitigation approaches rely either on high cost equipment or simplified models that have shown limitations for the complex ionospheric dynamics of the equatorial region. Data availability from increasingly larger GNSS receiver networks, together with scalable machine learning techniques, provides the foundation to approach this problem from a data driven perspective. This paper shows the usage of data driven models to predict the Vertical Total Electron Content (VTEC) of the ionosphere based on pseudoranges measurements and, thus, improve GNSS positioning using single frequency receivers especially in low latitudes. Our results reduce by more than 60% the error in VTEC prediction as compared to the Klobuchar model used by GPS, achieving an average positioning residual error below 1 meter, both in medium and low latitudes and in periods of high and low solar activity.