metadata only access

https://hdl.handle.net/2115/85146

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

英語

Bayesian optimization

2022-05

Elsevier

Acta astronautica

194

301-308

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.

学術雑誌論文

Version of Record

0094-5765