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.
Highlights GPS data in very low Earth orbit (VLEO) were measured in a deployable nanosatellite mission. Trajectory reconstruction methodology by machine learning based on Gaussian process was proposed. Coordinates and velocity in VLEO were reconstructed from nanosatellite’s sparse GPS data.
Trajectory reconstruction for nanosatellite in very low Earth orbit using machine learning
Acta Astronautica ; 194 ; 301-308
2022-02-15
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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