Prediction of GNSS Phase Scintillations: A Machine Learning Approach
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningPredictionSimilar Papers 제목 키워드 기반
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes. These interactions often manifest as aurora in the form of v…
Datasets of ionospheric parameters provided by SCINDA GNSS receiver from Lisbon airport area
Here we present datasets provided by a SCINDA GNSS receiver installed in the Lisbon airport area from November of 2014 to July of 2019. The installed equipment is a NovAtel EURO4 with a JAVAD Choke-Ring antenna. The data…
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series
This paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in diffe…
BIG-bench Machine LearningGaussian ProcessesPositionPrediction+3High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios
Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (…
PositionGraph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics
Graph Neural Networks (GNNs) have recently been explored as surrogate models for numerical simulations. While their applications in computational fluid dynamics have been investigated, little attention has been given to …