The dynamics of space objects in Low Earth Orbit (LEO) are strongly determined by the effects of atmospheric drag. This complex interaction, dependent on the physical properties of the atmosphere as well as of the objects themselves (e.g., mass, shape, material, attitude and orbit), comprises several of the primary sources of orbit uncertainty in LEO. The composition and density of the atmosphere is itself difficult to model, highly dependent on both altitude, and heating effects due to space weather conditions. Accurate calculation of the collision probability or re-entry time of a given object requires both an accurate prediction of the orbit, as well as an accurate estimation of its associated uncertainty. It is therefore fundamentally important to understand, characterise and ultimately reduce the underlying uncertainties in the atmospheric drag to perform better Space Traffic Management (STM).
The project focuses on the application of computational intelligence to these challenges, following a pipeline from space weather forecasting through to atmospheric density estimation and, ultimately, re-entry and conjunction analyses.