Mining Two-Line Element Data to Detect Orbital Maneuver for Satellite

Bai, Xiao; Liao, Chuan; Pan, Xiao; Xu, Ming

doi:10.1109/access.2019.2940248

Cited by 16 publications

(1 citation statement)

References 29 publications

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“…With the same purpose, Zhao et al [20] proposed a new fault diagnosis method of spacecraft based on time series data mining. We have also used data mining techniques to detect the orbital maneuvers of satellites at different scales in a previous study [21].…”

Section: Introductionmentioning

confidence: 99%

Correction of Atmospheric Model Through Data Mining With Historical Data of Two-Line Element

Bai

Liao

et al. 2020

IEEE Access

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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.

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Section: Introductionmentioning

confidence: 99%