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Multi-objective optimization using parallel simulation for space situational awareness

机译：使用并行模拟的多目标优化用于空间态势感知

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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 10~(22)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.
机译：改善太空态势感知（SSA）仍然是美国国防部（DoD）的首要任务之一。当前的研究表明，对地球同步轨道（GEO）SSA体系结构进行建模可以帮助识别地面和基于空间的传感器的最佳组合。本文通过扩展设计范围和完善方法来扩展以前的研究。使用多目标遗传算法来检查包含10〜（22）个可能的设计组合的增加的交易空间。优化器的结果显然有利于1.0 m孔径地面望远镜和0.15 m孔径传感器在12颗卫星地球同步极轨（GPO）星座中的组合。由于其他轨道受到40°日射角的限制，因此GPO制度提供了更多的进入GEO居住空间物体（RSO）的途径。当性能保持恒定时，与太阳同步轨道（SSO），赤道低地球轨道（LEO）和近地静止轨道（GEO）星座相比，GPO卫星星座可将总系统成本降低22.4％。并行高性能计算为解决国防部感兴趣的一类全新的复杂问题提供了可能。这项研究的结果可以教育国家政策制定者有关对当前和将来的SSA系统进行升级建议的好处。

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