Abstract
AbstractWe consider the problem of building the relationship of high-energy electron flux between Geostationary Earth Orbit (GEO) and Medium Earth Orbit (MEO). A time-series decomposition technique is first applied to the original data, resulting in trend and detrended part for both GEO and MEO data. Then we predict MEO trend with GEO data using three machine learning models: Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Experiment shows that RF gains best performance in all scenarios. Feature extraction analysis demonstrates that the inclusion of lagged features and (possible) ahead features is substantially helpful to the prediction. At last, an application of imputing missing values for MEO data is presented, in which RF model with selected features is used to handle the trend part while a moving block method is for the detrended part.
Highlights
It is well-known that high-energy electrons in the Earth’s outer radiation belt are crucial risk factors of satellite internal charging (Gubby and Evans 2002; Horne et al 2013)
Since the detrended part of Beidou data is mainly determined by orbit property of the Beidou Medium Earth Orbit (MEO) satellite and is not related to Geostationary Earth Orbit (GEO) observations, we only talk about predicting the trend of Beidou data with GOES trend data in the subsequent analysis
We proposed a machine learning approach that predicts Beidou MEO high-energy electron fluxes with its counterpart of GOES-15 satellite
Summary
It is well-known that high-energy electrons in the Earth’s outer radiation belt are crucial risk factors of satellite internal charging (Gubby and Evans 2002; Horne et al 2013). Such effect can subsequently cause significant satellites anomalies, leading to serious loss of service, such as communication interruption, navigation precision degradation, etc (Ryden et al 2008; Singh et al 2021). With an increasing number of satellites and its growing importance to our life, this topic has attracted a large amount of attention over past decades, resulting in lots of advances Most of these works focus on the Geostationary Earth Orbit (GEO) due to the large number of operational satellites and
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