Prediction of GNSS Phase Scintillations: A Machine Learning Approach
This addresses the need for accurate predictions of ionospheric disruptions for GNSS users, but it appears incremental as it builds on existing machine learning approaches without a new paradigm.
The paper tackled the problem of predicting GNSS phase scintillations caused by space weather, achieving state-of-the-art performance in forecasting magnitude one hour in advance within a ±5-minute window.
A Global Navigation Satellite System (GNSS) uses a constellation of satellites around the earth for accurate navigation, timing, and positioning. Natural phenomena like space weather introduce irregularities in the Earth's ionosphere, disrupting the propagation of the radio signals that GNSS relies upon. Such disruptions affect both the amplitude and the phase of the propagated waves. No physics-based model currently exists to predict the time and location of these disruptions with sufficient accuracy and at relevant scales. In this paper, we focus on predicting the phase fluctuations of GNSS radio waves, known as phase scintillations. We propose a novel architecture and loss function to predict 1 hour in advance the magnitude of phase scintillations within a time window of plus-minus 5 minutes with state-of-the-art performance.