CPS: Medium: Collaborative Research: Robust Capacity-Constrained Scheduling and Data-Based Model Refinement for Enhanced Collision Avoidance in Low-Earth Orbit

CPS：中：协作研究：稳健的容量受限调度和基于数据的模型细化，以增强低地球轨道的防撞能力

• 批准号: 1035250
• 负责人: Jeffrey Anderson
• 金额: $ 10.9万
• 依托单位: University Corporation For Atmospheric Res
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1035250&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Robust

The objective of this research is to improve the ability to track the orbits of space debris and thereby reduce the frequency of collisions. The approach is based on two scientific advances: 1) optimizing the scheduling of data transmission from a future constellation of orbiting Cubesats to ground stations located worldwide, and 2) using satellite data to improve models of the ionosphere and thermosphere, which in turn are used to improve estimates of atmospheric density. Intellectual MeritRobust capacity-constrained scheduling depends on fundamental research on optimization algorithms for nonlinear problems involving both discrete and continuous variables. This objective depends on advances in optimization theory and computational techniques. Model refinement depends on adaptive control algorithms, and can lead to fundamental advances for automatic control systems. These contributions provide new ideas and techniques that are broadly applicable to diverse areas of science and engineering.Broader ImpactsImproving the ability to predict the trajectories of space debris can render the space environment safer in both the near term---by enhancing astronaut safety and satellite reliability---and the long term---by suppressing cascading collisions that could have a devastating impact on the usage of space. This project will impact real-world practice by developing techniques that are applicable to large-scale modeling and data collection, from weather prediction to Homeland Security. The research results will impact education through graduate and undergraduate research as well as through interdisciplinary modules developed for courses in space science, satellite engineering, optimization, and data-based modeling taught across multiple disciplines.

这项研究的目的是提高跟踪空间碎片轨道的能力，从而减少碰撞的频率。 该方法基于两项科学进展：1）优化从未来轨道立方体卫星星座向世界各地地面站传输数据的时间安排; 2）利用卫星数据改进电离层和热层模型，进而用于改进对大气密度的估计。 智能化能力约束的鲁棒调度依赖于对涉及离散和连续变量的非线性问题的优化算法的基础研究。 这一目标取决于优化理论和计算技术的进步。 模型细化依赖于自适应控制算法，并且可以导致自动控制系统的根本进步。 这些贡献提供了广泛适用于科学和工程各个领域的新思想和新技术。更广泛的影响提高预测空间碎片轨迹的能力可以使空间环境在近期----通过提高宇航员的安全和卫星的可靠性----和长期----通过抑制可能对空间利用产生毁灭性影响的级联碰撞而更加安全。 该项目将通过开发适用于从天气预测到国土安全的大规模建模和数据收集的技术来影响现实世界的实践。研究结果将通过研究生和本科生研究以及为空间科学，卫星工程，优化和基于数据的建模课程开发的跨学科模块影响教育。