@article {Chennavi.581, author = {Jianping Chen and Yang Gao}, title = {Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning}, volume = {70}, number = {2}, elocation-id = {navi.581}, year = {2023}, doi = {10.33012/navi.581}, publisher = {Institute of Navigation}, abstract = {High-precision ionospheric corrections are essential for precise positioning using low-cost single-frequency GNSS receivers. Although Real-Time Global Ionosphere Maps (RT-GIMs) are available from the International GNSS Service (IGS), their ionospheric predictions continue to rely on networks of globally-distributed GNSS stations and real-time data links. In this paper, we develop a regional real-time ionospheric prediction model based on a long short-term memory (LSTM) deep learning method. Because the GIMs from the IGS are used as prediction bases, the requirement for real-time GNSS data-links is eliminated. A comparison of the ionospheric predictions generated over 24 hours by the proposed method and the IGS GIM revealed a prediction accuracy root mean square error of 0.8 TECU. These results suggest that the proposed model may be suitable for use in real-time applications.}, issn = {0028-1522}, URL = {https://navi.ion.org/content/70/2/navi.581}, eprint = {https://navi.ion.org/content/70/2/navi.581.full.pdf}, journal = {NAVIGATION: Journal of the Institute of Navigation} }