摘要
为保证航天器在对地观测过程中任务分配均衡合理,且执行过程中尽可能节约燃料、节省时间,面向航天器对地观测任务规划的研究成为国际热点。重点围绕该领域研究现状与发展趋势,开展了技术研究综述。首先,介绍卫星对地观测任务需求筹划技术。其次,阐释了面向航天器对地观测任务规划建模与算法的研究,将规划算法分为传统优化算法、智能优化算法以及机器学习算法3种,对不同规划模型以及3类规划算法展开了详细介绍。最后,探讨了对地观测任务规划技术的发展趋势并提出展望。研究航天器对地观测任务规划问题能够为工程实践提供理论支撑,为军事观测、导航规划、灾害防治、国土测绘等众多方面的活动提供技术支持。
A systematic review is conducted on the research status and development trends of spacecraft Earth observation mission planning,aiming to ensure uniform task allocation and fuel/time efficiency during mission execution.Firstly,satellite Earth observation mission requirement planning technologies are introduced.Subsequently,modeling approaches and algorithms for spacecraft Earth observation mission planning are elaborated,with planning algorithms categorized into three groups:traditional optimization algorithms,intelligent optimization algorithms,and machine learning-based approaches.Detailed analyses are provided for different planning models and the three algorithm categories.Finally,development trends and future prospects for Earth observation mission planning technologies are discussed.Research in this domain provides theoretical foundations for engineering practices and technical support for applications including military surveillance,navigation planning,disaster management,and territorial mapping.
作者
徐明
郝雅波
白雪
张锐
胡志强
李宝卫
XU Ming;HAO Yabo;BAI Xue;ZHANG Rui;HU Zhiqiang;LI Baowei(School of Astronautics,Beihang University,Beijing 100191,China;Shen Yuan College,Beihang University,Beijing 100191,China;Shanghai Satellite Internet Research Institute,Shanghai 200131,China;Shanghai Satellite Internet Key Laboratory,Shanghai 200131,China;Bayuan Yunjian(Beijing)Aerospace Technology Research Institute Co.,Ltd,Beijing 100191,China)
出处
《宇航学报》
北大核心
2025年第8期1501-1518,共18页
Journal of Astronautics
基金
上海卫星互联网重点实验室开放课题。
关键词
任务规划
敏捷卫星
对地观测
智能优化
机器学习
Mission planning
Agile satellite
Earth observation
Intelligent optimization algorithm
Machine learning
