|M.Sc Student||Mazal Leonel|
|Subject||Optimization of Regional Constellations Establishment and|
|Department||Department of Aerospace Engineering||Supervisor||Professor Emeritus Mauricio Guelman|
|Full Thesis text|
In recent years, the increased interest in designing satellite constellations in medium and low orbits is related to the growing importance of their use in telecommunication, navigation and remote sensing. Higher resolution imaging for Earth observation, reduced transmission power requirements, and signal time delays are the main advantages of deploying low and medium Earth orbit satellite constellations with respect to geostationary platforms. On the other hand, the motion relative to the Earth’s surface introduces several complexities and methods for capturing the performance of constellation architecture are needed. Common classes of constellations show a high degree of symmetry and are particularly suitable for continuous coverage of large areas. There are however, instances where only adequate coverage is needed.
This thesis deals with constellation optimization. When partial coverage is allowed, one of the most important figures of merit is the maximal time gap. This is the longest time interval in which the target is not visible by any satellite. This work aims at minimizing the maximal time gap (MTG) for a pre-selected site with minimal restrictions on the satellite constellation configuration. The coverage characteristics of these intermittent constellations present many difficulties in the form of a complex design space. Genetic Algorithms (GA) have the ability to handle complex design spaces that do not have convenient analytical representations. In this thesis a methodology is developed for the optimization of local satellite constellations, first for the ideal two-body problem then, the same principle is extended to consider perturbations due to Earth's oblateness.
In addition to the optimization of constellation configurations, the problem of reconfiguring the constellation is studied. For a constellation already deployed to cover a certain region, the design of reconfiguration methodologies to achieve new objectives in terms either of spatial or temporal coverage is required. A method to optimize the fuel expenditure of transferring a whole constellation to perform a new mission is presented. This method is based on the Hungarian and Genetic Algorithms. Representative results for both constellation optimization and reconfiguration are presented to demonstrate the validity and potential value of the various developed methods.