DOAJ Open Access 2023

Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning

Jianping Chen Yang Gao

Abstrak

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 datalinks 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.

Topik & Kata Kunci

Canals and inland navigation. Waterways Naval Science

Penulis (2)

Jianping Chen

Yang Gao

Format Sitasi

APA MLA BibTeX

Chen, J., Gao, Y. (2023). Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning. https://doi.org/10.33012/navi.581

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.33012/navi.581
Akses: Open Access ✓