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Near Optimal Ground Support in Multi-Spacecraft Missions: A GA Model and its Results

机译:多航天器任务中的近乎最佳地面支持:遗传算法模型及其结果

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摘要

This study addresses the optimal allotment of ground station support time to low Earth orbit (LEO) spacecraft with clashing radio visibilities. LEOs now form a critical global infrastructure for natural resource management, rescue, crop yield estimation, flood control, communication, and space research and travel support. In the multi-spacecraft scenario, ground support becomes complex because of spacecraft-specific constraints, station configuration, spacecraft priorities and priorities of payload and special operations. A generalization of the classical product mix problem, spacecraft support is NP-complete and more complex than the former because of arbitrarily defined profitability profile. Genetic algorithms (GA) are used to near optimally resolve visibility clashes. It concludes with the illustration of real life spacecraft support optimization problems routinely faced by mission managers. A spin-off of this work is that it can enable the decision maker to also determine optimal ground station locations and support capability deployment in diverse planning scenarios.
机译:这项研究解决了在无线电可见性冲突的情况下,将地面站支持时间分配给低地球轨道(LEO)航天器的最佳方案。 LEO现在构成了用于自然资源管理,救援,作物产量估算,防洪,通信以及太空研究和旅行支持的重要全球基础设施。在多航天器场景中,由于航天器特定的约束,空间站配置,航天器优先级以及有效载荷和特殊操作的优先级,地面支持变得复杂。作为经典产品组合问题的概括,由于任意定义的获利能力概况,航天器支持是NP完全的,并且比前者更复杂。遗传算法(GA)用于近乎最佳地解决可见性冲突。最后以任务管理人员经常遇到的现实生活中的航天器支持优化问题为例进行说明。这项工作的一个副产品是,它可以使决策者还可以确定最佳的地面站位置,并支持在各种计划方案中进行能力部署。

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