Abstract
The existing atmospheric mass density models (AMDM) would produce considerable errors in orbital prediction for Low Earth Orbit (LEO) satellites. In order to reduce these errors and correct the AMDM, this paper presents methods based on data mining with historical data of two-line element (TLE). Starting from a typical LEO satellite, TIANHUI, two orbital dynamical models are firstly proposed as the simulation environment to generate training data. The historical TLE data are regarded as actual space environment and used to generate application data. Secondly, three data mining methods, Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM), are combined with the training data to investigate their feasibility in recovering the known deviation of AMDM under simulation environment. Training results show that RF displays the best performance and achieves the accuracy of 99.99%, while the other two methods only achieve 86.83% and 71.90% respectively. Thirdly, under the actual space environment, this paper uses new training and application data to research the ability of the three methods in recovering the unknown deviation of the AMDM and improve the accuracy of orbital prediction. Numerical results are evidential to the accuracy of the proposed methods based on data mining. It is concluded that the capabilities of the data mining for correction for the atmospheric model are very promising, with great potential to advance practical applications on on-orbit propagation.
Highlights
Data mining, known as Knowledge Discovery in Database (KDD), was first emerged in the 1980s and got rapid development in the 1990s
Based on the historical two-line element (TLE) data of TIANHUI satellite, this paper investigates the feasibility of data mining technology in recovering the deviation of the existing atmospheric mass density models (AMDM)
This paper focuses on the orbital prediction of TIANHUI and data mining technology
Summary
Known as Knowledge Discovery in Database (KDD), was first emerged in the 1980s and got rapid development in the 1990s. Data mining technology is good at dealing with a large amount of data to excavate those hidden, short-term but potentially useful, providing new techniques to improve the accuracy of the AMDM instead of replacing them and benefit the orbital prediction of LEO satellite. We make four contributions in this paper listed as following: 1) It is proved that data mining provides a practical technique to refine valuable information of orbital elements as well as atmospheric density model from historical TLE data. 2) The feasibility of data mining in predicting the AMDM errors is verified in simulation environment provided by the accurate orbital dynamical model and the simplified model with deviation. These noises are not the points discussed in this paper
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