Spatiotemporal Deep Learning Network for High-Latitude Ionopsheric Phase Scintillation Forecasting

Yunxiang Liu, Zhe Yang, Y. T. Jade Morton, Ruoyu Li

Abstract: In this paper, we present a spatiotemporal deep learning (STDL) network to conduct the binary phase scintillation forecast at a high latitude GNSS (global navigation satellite system) station. Historical measurements from the target and surrounding GNSS stations are utilized. In addition, external features such as solar wind parameters and geomagnetic activity indices are also included. The results show that the STDL network adaptively incorporates spatiotemporal and external information to achieve the best performance by outperforming 5 other methods.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 3920 - 3931
Cite this article: Liu, Yunxiang, Yang, Zhe, Morton, Y. T. Jade, Li, Ruoyu, "Spatiotemporal Deep Learning Network for High-Latitude Ionopsheric Phase Scintillation Forecasting," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 3920-3931.
https://doi.org/10.33012/2021.18061
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