AI-driven versus traditional ionospheric modeling approaches for GNSS positioning in Egypt

Abstract

Abstract The ionosphere significantly impacts Global Navigation Satellite Systems (GNSS) positioning accuracy, particularly in regions with pronounced ionospheric irregularities and high solar activity, such as Egypt. These regions face more challenges in ionospheric modeling compared to the higher-latitude areas. This study provides a detailed assessment of different approaches to Total Electron Content (TEC) estimation in Egypt and their impact on GNSS positioning accuracy using single-frequency receivers. The approaches include the traditional Klobuchar model, Global Ionospheric Maps (GIMs), and the Artificial Neural Network (ANNTEC) model. This ANN model was developed to predict the TEC over Egypt using 10 years of GNSS observations and ionospheric data. The assessment process is performed in two scenarios: static and kinematic positioning. The positioning accuracy employing each approach is evaluated relative to reference coordinates tied to the International Terrestrial Reference Frame 2020 (ITRF-2020) through the nearest International GNSS Service (IGS) stations. The results indicate that the ANNTEC model outperforms the other approaches in both kinematic and static scenarios. In the static mode, the Root Mean Square Error (RMSE) value of the horizontal positions can be improved by 46.6 % and 24.5 % compared to using the Klobuchar model and the GIMs, respectively. In addition, in the kinematic mode, the RMSE value was reduced by 54.7 % and 25.8 % compared to using the Klobuchar model and the GIMs, respectively. These results demonstrate the potential of employing the ANNTEC model to enhance single-frequency GNSS positioning accuracy in Egypt.