卫星编队配置优化设计：一组混合整数线性规划的集合
Optimal Satellite Constellation Configuration Design: A Collection of Mixed Integer Linear Programs
AI总结：
本文提出一组混合整数线性规划，用于优化卫星编队配置，能够处理多种指标和任务场景，提供可证明最优的编队方案。
摘要：
设计卫星编队系统涉及复杂的多学科优化，其中覆盖面积是整体系统成本和性能的主要驱动因素。在编队配置设计中，覆盖面积可能作为优化目标或约束，取决于任务目标。现有文献针对每个任务场景进行案例分析，采用不同的假设、建模技术和求解方法。虽然这些针对特定问题的方法提供了有价值的见解，但用户在进行不同任务场景之间的权衡研究时往往面临实施挑战，因为每个场景必须单独处理。本文提出一组五个混合整数线性规划，具有实际意义，可通过添加约束扩展到更复杂的任务叙述，并能够获得可证明最优的编队配置。该框架可以处理多种指标和任务场景，如百分比覆盖、平均或最大 revisit 时间、固定数量的卫星、时空变化的覆盖需求以及静态或动态目标。本文通过几个案例研究和比较分析来展示所提框架的通用性。
英文摘要：
Designing satellite constellation systems involves complex multidisciplinary optimization in which coverage serves as a primary driver of overall system cost and performance. Among the various design considerations, constellation configuration, which dictates how satellites are placed and distributed in space relative to each other, predominantly determines the resulting coverage. In constellation configuration design, coverage may be treated either as an optimization objective or as a constraint, depending on mission goals. State-of-the-art literature addresses each mission scenario on a case-by-case basis, employing distinct assumptions, modeling techniques, and solution methods. While such problem-specific approaches yield valuable insights, users often face implementation challenges when performing trade-off studies across different mission scenarios, as each scenario must be handled distinctly. In this paper, we propose a collection of five mixed-integer linear programs that are of practical significance, extensible to more complex mission narratives through additional constraints, and capable of obtaining provably optimal constellation configurations. The framework can handle various metrics and mission scenarios, such as percent coverage, average or maximum revisit times, a fixed number of satellites, spatiotemporally varying coverage requirements, and static or dynamic targets. The paper presents several case studies and comparative analyses to demonstrate the versatility of the proposed framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
