Electro-Optic Satellite Constellation Design Using Multi-Objective Genetic Algorithm

Satellite constellation design is a complex, highly constrained, and multidisciplinary problem. Unless optimization tools are used, tradeoffs must be conducted at the subsystem level resulting in feasible, but not necessarily optimal, system designs. As satellite technology advances, new methods to optimize the system objectives are developed. This study is based on the development of a representative regional remote sensing constellation design. This thesis analyses the design process of an electro-optic satellite constellation with regional coverage considerations using system-level optimization tools. A multi objective genetic algorithm method is used to optimize the constellation design by utilizing MATLAB and STK integration. Cost, spatial resolution, and coverage are computed as objective functions. A single variable Space Telescope Cost Model is used to determine the system cost. The search parameters of the optimization method are the 6 classical orbital elements, Walker constellation parameters such as number of planes and number of satellites per plane, and the sensor diameter length as the driving variable for the cost model. The results from this model will provide a trade-space for the baseline satellite design based on the sensors diameter length and cost, versus mission requirements. Resulting tradeoffs allow decision makers to have a broad perspective of constellation usage for remote sensing missions for their preferences.