电子与控制

敏捷成像卫星多星密集任务调度方法

  • 邱涤珊 ,
  • 郭浩 ,
  • 贺川 ,
  • 伍国华
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  • 国防科学技术大学 信息系统工程重点实验室, 湖南 长沙 410073
邱涤珊 男, 博士, 教授, 博士生导师。主要研究方向: 作战模型与模拟, 组合优化, 天基信息系统, 卫星应用信息链。 Tel: 0731-84573589 E-mail: ds_qiu@sina.com;郭浩 男, 博士研究生。主要研究方向: 作战模型与模拟、 卫星规划调度方法、 卫星系统效能分析。 Tel: 0731-84573590 E-mail: guohao@nudt.edu.cn;贺川 男, 博士研究生。主要研究方向: 作战模型与模拟, 智能计算, 组合优化, 天基资源规划调度技术。 Tel: 0731-84573590 E-mail: chuanhe@nudt.edu.cn;伍国华 男, 博士研究生。主要研究方向: 作战模型与模拟, 智能计算, 组合优化, 天基资源规划调度技术。 Tel: 0731-84573590 E-mail: guohuawu@nudt.edu.cn

收稿日期: 2012-04-27

  修回日期: 2012-11-23

  网络出版日期: 2013-04-23

基金资助

国家自然科学基金(61104180;71271213)

Intensive Task Scheduling Method for Multi-agile Imaging Satellites

  • QIU Dishan ,
  • GUO Hao ,
  • HE Chuan ,
  • WU Guohua
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  • Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China

Received date: 2012-04-27

  Revised date: 2012-11-23

  Online published: 2013-04-23

Supported by

National Natural Science Foundation of China (61104180, 71271213) *Corresponding author. Tel.: 0731-84573589 E-mail: ds_qiu@sina.com

摘要

面向应急观测需求,对敏捷成像卫星多星密集点目标观测任务调度问题进行研究。针对敏捷成像卫星观测特点,综合考虑卫星可观测时间窗口、任务间卫星姿态调整时间、卫星最长连续工作时间、星上存储容量、卫星能量等约束,建立多星任务调度模型。提出了一种改进的蚁群优化(ACO)算法对调度模型进行求解。该算法借鉴了蚁群系统(ACS)和最大最小蚂蚁系统(MMAS)的思想,结合调度相关约束设计寻优策略和信息素更新策略。引入任务优先级、最早及最晚可观测时间等因素来控制转移概率。仿真结果验证了模型和算法的有效性。

本文引用格式

邱涤珊 , 郭浩 , 贺川 , 伍国华 . 敏捷成像卫星多星密集任务调度方法[J]. 航空学报, 2013 , 34(4) : 882 -889 . DOI: 10.7527/S1000-6893.2013.0149

Abstract

Considering the observing request in an emergency, intentive observing task scheduling of multi-agile imaging satellites is studied. A scheduling model is established which considers such complex constraints as the visible time window, the attitude changing duration between tasks, the maximal successive working duration, energy and storage capacity restriction, etc. An improved ant colony optimization (ACO) algorithm is designed to solve the problem, which is based on ant colony system (ACS) and max-min ant system (MMAS). The searching strategy and pheromone update strategy are designed according to the scheduling constraints. The factors of task priority and bounds of the visible time are introduced into transfer rules to control the transition probability. Simulation results show the effectiveness and efficiency of our approach.

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