Construction of a methodology to estimate atmospheric density using machine learning based on data measured by nanosatellite

Abstract

The atmospheric density in very low Earth orbit (VLEO) is a crucial factor for nano/small satellite missions. However, the profile of the atmospheric density is not clear yet because of the difficulty to obtain data in VLEO. In the duration of 2016—2017, The deployable nanosatellite EGG mission was conducted, and its sparse coordinate data was obtained by the global positioning system (GPS). To utilize this data for atmospheric density estimation, we constructed a methodology to estimate atmospheric density based on Gaussian process regression and Bayesian optimization, and estimated atmospheric density at altitudes around 200 km.