Optimization of Geosynchronous Space Situational Awareness Architectures using Parallel Computation

Improving Space Situational Awareness SSA remains one of the DoDs top priorities. Current research has shown that modeling of GEO SSA architectures can identify optimal combinations of ground and space-based sensors. This thesis extends previous research by expanding design boundaries and refining the methodology. A genetic algorithm examined this increased trade space containing 1022 possible architectures. Experimental trials that would have taken over 100 years on a desktop computer were completed in weeks using a high-performance computer containing over125,000 cores. The results of the optimizer clearly favor 1.0-meter aperture ground telescopes combined with 0.15-meter aperture sensors in a 12-satellite polar GEO constellation. The 1.0-meter aperture ground telescopes have the best cost-performance combination for detecting Resident Space Objects RSOs in GEO. The polar GEO regime offers increased access to GEORSOs since other orbits are restricted by the 40 solar exclusion angle. When performance is held constant, a polar GEO satellite constellation offers a 22.4 reduction in total system cost when compared to Sun Synchronous Orbit, equatorial LEO, and near GEO constellations. The results of this research can be used to educate national policy makers on the benefits of various proposed upgrades to current and future SSA systems.