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 \corrA{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.
翻译:电离层电磁活动是卫星通信、全球导航卫星系统和其他重要空间应用质量的一个主要因素。如果能够在全球预测总电子内容,就可以更好地预测潜在的性能退化。一些研究提出了能够在当地预测技术执委会的模式,但大多数情况下并不是全世界。由于过去公开提供的技术执委会地图记录很大,我们提议了一种基于深神经网络的方法,用以预测一系列全球技术执委会地图,这些图与技术执委会地图的输入序列相接,而不引入任何先前的知识\corrA{(除地球旋转周期外)。通过将一些最先进的结构结合起来,拟议的方法与以往的技术执委会预测工作具有竞争力,同时对全球技术执委会预测进行全球预测。