Abstract
The continuation of space activities is at risk due to the growing number of uncontrolled objects, called space debris, which can collide with operational spacecraft. In addition, debris can fall back to the Earth causing risks to the population. Therefore, space agencies have started space situational awareness (SSA) programs and taken space debris mitigation measures to reduce the risks caused by uncontrolled objects and prevent the generation of new debris. A fundamental need for SSA is the capability to predict, design and analyse orbits. In this work, new techniques for orbit prediction are developed that are suitable for SSA in terms of accuracy, efficiency and ability to deal with uncertainties and are applied for re-entry prediction, end-of-life disposal, ADR mission design and long-term orbit prediction. The performance of high-order Poincaré mapping of perturbed orbits is improved by introducing a new set of orbital elements and the method is applied for orbit propagation and analysis of quasi-periodic orbits. Two new Lambert problem solvers are developed to compute perturbed rendezvous trajectories with hundreds of revolutions for the design of active debris removal missions. The computation of the effect of drag for semi-analytical propagation is speed up by using high-order Taylor expansions to evaluate the mean element rates efficiently. In addition, the high-order expansion of the flow through semi-analytical propagation is enabled using differential algebra to allow efficient propagation of initial conditions. The predictability of Galileo disposal orbits was investigated using chaos indicators and sensitivity analysis. The study showed that the orbits are predictable and that chaos indicators are not unsuitable for predictability analysis. Finally, to improve the re-entry prediction of rocket bodies based on two-line element data, ballistic coefficient and state estimation methods are enhanced. Using the developed approach, the re-entry prediction using only a ballistic coefficient estimate was found to be as accurate as re-entry prediction after full state estimation.