PT  - JOURNAL ARTICLE
AU  - Jianping Chen
AU  - Yang Gao
TI  - Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning
AID  - 10.33012/navi.581
DP  - 2023 Jun 20
TA  - NAVIGATION: Journal of the Institute of Navigation
PG  - navi.581
VI  - 70
IP  - 2
4099  - https://navi.ion.org/content/70/2/navi.581.short
4100  - https://navi.ion.org/content/70/2/navi.581.full
SO  - NAVIGATION2023 Jun 20; 70
AB  - 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.