Over the last decade, the number of Earth observation satellites in orbit have exponentially increased, many of which are flown in constellations. This development necessitates the improvement of mission analysis tools, particularly in the design of satellite constellations.
This thesis provides a new, improved coverage analysis method and applies it for research on constellation design optimization. More specifically, the new coverage analysis method is used to evaluate the revisit time and discontinuous coverage of any constellation configuration. Using a semi-analytical ground-to-spacecraft calculation, the coverage analysis method proves to be both accurate and fast.
In optimization, the coverage analysis method can be used to evaluate the candidate constellation configurations. By applying the method in a satellite constellation optimization framework, various optimization algorithms could be researched and compared. It was found that the choice of optimization algorithm is dependent on the desired result of the user, such as providing one singular optimal constellation or providing a wider range of viable configurations. The genetic algorithm and the covariance matrix adaptation evolution strategy proved to perform the best in single objective optimization, while the non-dominated sorting genetic algorithm II proved a good alternative for multi objective optimization.