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Trajectory reconstruction for nanosatellite in very low Earth orbit using machine learning

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Title: Trajectory reconstruction for nanosatellite in very low Earth orbit using machine learning
Authors: Takahashi, Yusuke Browse this author →KAKEN DB
Saito, Masahiro Browse this author
Oshima, Nobuyuki Browse this author →KAKEN DB
Yamada, Kazuhiko Browse this author
Keywords: Satellite trajectory
Inflatable aerodynamic decelerator
Gaussian process regression
Bayesian optimization
Issue Date: May-2022
Publisher: Elsevier
Journal Title: Acta astronautica
Volume: 194
Start Page: 301
End Page: 308
Publisher DOI: 10.1016/j.actaastro.2022.02.010
Abstract: Micro-and nanosatellites are launched into very low Earth orbit (VLEO). However, the atmospheric density in VLEO is unclear, making it difficult to predict the satellite behavior and lifetime. During a stay-in-orbit and reentry mission of a nanosatellite, global positioning system (GPS)-based positioning was performed at 400 to 100 km using the Iridium satellite network. This can provide useful insights for estimating the atmospheric density in VLEO. However, the intermittency of the GPS data made these estimations difficult. We performed Gaussian process regression (GPR) by using GPS data as training data and reconstructed continuous data from the sparse positioning data. The proposed reconstruction method was flexible in selection of the kernel function. The velocity profile of the satellite was reconstructed using the obtained GPR results, equation of motion simulation of a three-degree-of-freedom mass-point system in a non-inertial coordinate system, and Bayesian optimization. The prediction performance and characteristics of trajectory reconstruction by GPR were verified.
Type: article
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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