Multi-objective optimization using parallel simulation for space situational awareness

Abstract

Improving space situational awareness (SSA) remains one of the Department of Defense’s (DoD) top priorities. Current research has shown that the modeling of geosynchronous orbit (GEO) SSA architectures can help identify optimal combinations of ground- and space-based sensors. This paper extends previous research by expanding design boundaries and refining the methodology. A multi-objective genetic algorithm was used to examine this increased trade-space containing 10²² possible design combinations. The results of the optimizer clearly favor 1.0 m aperture ground telescopes combined with 0.15 m aperture sensors in a 12-satellite geosynchronous polar orbit (GPO) constellation. The GPO regime offers increased access to GEO resident space objects (RSO) since other orbits are restricted by a 40° solar exclusion angle. When performance is held constant, a GPO satellite constellation offers a 22.4% reduction in total system cost when compared to Sun synchronous orbit (SSO), equatorial low earth orbit (LEO), and near-GEO constellations. Parallel high-performance computing provides the possibility of solving an entirely new class of complex problems of interest to the DoD. The results of this research can educate national policy makers on the benefits of proposed upgrades to current and future SSA systems.

Keywords

High performance computing parallel simulation system analysis and design multi-objective optimization space situational awareness

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