AI-driven versus traditional ionospheric modeling approaches for GNSS positioning in Egypt
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Ahmed Abdelmaaboud
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Tamer Fathallah
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.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Data used in this paper is available from the authors upon request.
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