<p>The accuracy and reliability of Global Navigation Satellite System (GNSS) applications are affected by the state of the Earth&#8216;s ionosphere, especially when using single frequency observations, which are employed mostly in mass-market GNSS receivers. In addition, space weather can be the cause of strong sudden disturbances in the ionosphere, representing a major risk for GNSS performance and reliability. Accurate corrections of ionospheric effects and early warning information in the presence of space weather are therefore crucial for GNSS applications. This correction information can be obtained by employing a model that describes the complex relation of space weather processes with the non-linear spatial and temporal variability of the Vertical Total Electron Content (VTEC) within the ionosphere and includes a forecast component considering space weather events to provide an early warning system. To develop such a model is challenging but an important task and of high interest for the GNSS community.</p><p>To model the impact of space weather, a complex chain of physical dynamical processes between the Sun, the interplanetary magnetic field, the Earth's magnetic field and the ionosphere need to be taken into account. Machine learning techniques are suitable in finding patterns and relationships from historical data to solve problems that are too complex for a traditional approach requiring an extensive set of rules (equations) or for which there is no acceptable solution available yet.</p><p>The main objective of this study is to develop a model for forecasting the ionospheric VTEC taking into account physical processes and utilizing state-of-art machine learning techniques to learn complex non-linear relationships from the data. In this work, supervised learning is applied to forecast VTEC. This means that the model is provided by a set of (input) variables that have some influence on the VTEC forecast (output). To be more specific, data of solar activity, solar wind, interplanetary and geomagnetic field and other information connected to the VTEC variability are used as input to predict VTEC values in the future. Different machine learning algorithms are applied, such as decision tree regression, random forest regression and gradient boosting. The decision trees are the simplest and easiest to interpret machine learning algorithms, but the forecasted VTEC lacks smoothness. On the other hand, random forest and gradient boosting use a combination of multiple regression trees, which lead to improvements in the prediction accuracy and smoothness. However, the results show that the overall performance of the algorithms, measured by the root mean square error, does not differ much from each other and improves when the data are well prepared, i.e. cleaned and transformed to remove trends. Preliminary results of this study will be presented including the methodology, goals, challenges and perspectives of developing the machine learning model.</p>
Machine Learning Algorithms for Spacecraft Magnetic Field Interference Cancellation: Enabling Satellite Magnetometry without a Boom
