RT Journal Article
SR Electronic
T1 Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning
JF NAVIGATION: Journal of the Institute of Navigation
JO NAVIGATION
FD Institute of Navigation
SP navi.581
DO 10.33012/navi.581
VO 70
IS 2
A1 Jianping Chen
A1 Yang Gao
YR 2023
UL https://navi.ion.org/content/70/2/navi.581.abstract
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.