中国空间科学技术 ›› 2020, Vol. 40 ›› Issue (2): 17-.doi: 10.16708/j.cnki.1000-758X.2020.0015

• 研究探讨 • 上一篇    下一篇

应急任务响应时间最优的多星成像规划方法

陈书剑,李智,胡敏,张雅声   

  1. 航天工程大学,北京101400
  • 出版日期:2020-04-25 发布日期:2020-04-26

Multi satellite imaging planning method with optimal response time for emergency tasks

CHEN Shujian,LI Zhi,HU Min,ZHANG Yasheng   

  1. Space Engineering University,Beijing101400,China
  • Published:2020-04-25 Online:2020-04-26

摘要: 针对优化多星应急成像任务规划的响应时间问题进行了研究。为避免优先规划应急任务对任务总收益的影响,提出一种优化应急任务响应时间的同时兼顾任务总收益的多星成像规划方法。首先,针对综合考虑应急任务和常规任务的多星成像规划特点,建立两级目标优化的约束满足模型;其次,将模型求解过程分解为任务时间窗选择和单轨动态规划两个部分,基于自适应免疫算法对时间窗选择进行优化,同时设计前向动态规划算法确定卫星单轨最优观测路径;最后,对所设计算法的性能进行了测试,并与其他算法进行了对比。仿真结果表明本文方法能够保证应急任务响应时间最优,并同时具备较高的任务总收益,适合于求解大规模的多星成像规划问题。

关键词: 成像卫星, 应急任务, 响应时间, 免疫算法, 动态规划

Abstract: The problem of response time in multisatellite imaging planning for emergency tasks was investigated. To avoid the influence of preferentially planning of emergency tasks on total task revenue, a multisatellite imaging planning method was proposed, with which the response time of emergency tasks was optimized and the total task revenue was considered. Firstly, according to the characteristics of multisatellite imaging planning which considers both emergency and routine tasks, a constraint satisfaction model for twolevel target optimization was established. Secondly, the model solving process was divided into two parts,i.e., selection of time window and dynamic planning on single orbit. The selection of time window was optimized based on adaptive immune algorithm, and the forward dynamic planning algorithm was designed to determine the optimal path of satellite on single orbit. Finally, the designed algorithm was tested and compared with other algorithms. Simulation results demonstrate that the proposed method can ensure the shortest response time of emergency tasks and get relatively high total task revenue in largescale problem of multisatellite imaging planning.

Key words: imaging satellite, emergency task, response time, immune algorithm, dynamic programming