Ionospheric activity prediction using convolutional recurrent neural networks
This addresses the need for global ionospheric activity prediction for satellite and navigation systems, but it is incremental as it combines existing architectures.
The paper tackles the problem of forecasting global Total Electron Content (TEC) maps to anticipate performance degradations in satellite telecommunications and GNSS, achieving competitive results with previous works while predicting TEC globally.
The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications. Being able to forecast globally the Total Electron Content (TEC) would enable a better anticipation of potential performance degradations. A few studies have proposed models able to predict the TEC locally, but not worldwide for most of them. Thanks to a large record of past TEC maps publicly available, we propose a method based on Deep Neural Networks (DNN) to forecast a sequence of global TEC maps consecutive to an input sequence of TEC maps, without introducing any prior knowledge other than Earth rotation periodicity. By combining several state-of-the-art architectures, the proposed approach is competitive with previous works on TEC forecasting while predicting the TEC globally.