分布式空间干涉测量任务的恒星观测调度优化
Stellar Observation Scheduling Optimization for Distributed Space Interferometry Missions
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中文总结 AI 辅助
针对分布式空间干涉测量任务，提出多目标动态规划方案，利用任务设计见解优化燃料消耗与科学成果平衡，考虑多种条件生成全局最优策略，是多目标优化在该领域的首次应用。
中文摘要 AI 辅助
对用于探测系外行星生物特征的空间干涉测量的兴趣日益浓厚，促使人们开发了几种低成本任务概念，采用地球轨道编队飞行技术对来自系外行星系统的入射光进行消零干涉测量。在此基础上，本文提出了一种多目标动态规划方案，通过利用任务设计的见解来优化低成本编队飞行空间干涉测量任务的燃料消耗和科学成果之间的平衡。该方案考虑了名义操作概念约束、燃料消耗和可观测性条件，以生成用于系外行星系统观测的全局最优帕累托前沿策略，是多目标优化在分布式空间望远镜天文学中的首次应用之一。
英文摘要
Growing interest in space interferometry for detecting bio-signatures of exoplanets has led to the development of several low-cost mission concepts involving Earth-orbiting formation flying techniques to perform nulling interferometry of incoming light from exoplanetary systems. Pursuing these developments, this work proposes a multi-objective dynamic programming scheme to optimize the balance between fuel expenditure and scientific outcomes of low-cost formation flying space interferometry missions by leveraging several insights from mission design. This scheme accounts for nominal concept of operations constraints, fuel expenditure, and observability conditions to produce a globally optimal Pareto front policy for exoplanetary system observation, and represents one of the first applications of multi-objective optimization to distributed space telescope astronomy.
