计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 242-247.doi: 10.11896/jsjkx.210500093

• 计算机网络 • 上一篇    下一篇

基于注意力神经网络的对地观测卫星星上自主任务规划方法

彭双, 伍江江, 陈浩, 杜春, 李军   

  1. 国防科技大学电子科学学院 长沙410073
  • 收稿日期:2021-05-13 修回日期:2021-09-08 出版日期:2022-07-15 发布日期:2022-07-12
  • 通讯作者: 陈浩(hchen@nudt.edu.cn)
  • 作者简介:(pengshuang08@nudt.edu.cn)
  • 基金资助:
    国家自然科学基金(62106276);湖南省自然科学基金(2020JJ4103)

Satellite Onboard Observation Task Planning Based on Attention Neural Network

PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun   

  1. College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China
  • Received:2021-05-13 Revised:2021-09-08 Online:2022-07-15 Published:2022-07-12
  • About author:PENG Shuang,born in 1990,Ph.D,lecturer,is a member of China Computer Federation.His main research interests include satellite intelligent scheduling and machine learning.
    CHEN Hao,born in 1982,Ph.D,professor,master supervisor,is a member of China Computer Federation.His main research interests include computational intelligence,machine learning and satellite intelligent scheduling.
  • Supported by:
    National Natural Science Foundation of China(62106276) and Natural Science Foundation of Hunan Province(2020JJ4103).

摘要: 星上自主任务规划是对地观测卫星自主运行的关键技术之一,近年来得到了研究人员的高度关注。考虑到星上计算资源有限,以及星上任务、资源动态变化等特点与挑战,现有研究主要采用启发式搜索算法对卫星星上自主任务规划问题进行求解,但这类算法还有待进一步优化。文中首先构建了一种新的观测任务序贯决策框架。基于该框架,对地观测卫星可以实时决策要执行的观测任务,而无须预先生成任何观测方案。然后,将注意力机制和循环神经网络相结合,设计了观测任务决策模型、任务特征表示方法以及模型训练方法,提出了一种基于注意力神经网络的观测任务序贯算法;最后,基于多组随机数据对所提算法、两种深度学习算法以及两种启发式在线搜索算法进行了比较。实验结果表明,所提方法的平均响应时间不到已有深度学习算法的1/5,收益误差远低于启发式搜索算法,证实了所提方法的可行性和有效性。

关键词: 对地观测卫星, 星上自主任务规划, 序贯决策, 循环神经网络, 注意力机制

Abstract: Satellite onboard autonomous task planning is one of the key technologies for the operation of earth observation satellites,which has received great attention from researchers in recent years.Considering the limited computing resources,as well as the dynamic changes of observation tasks and resource onboard,the heuristic search algorithms are mainly used to solve the satellite onboard task planning problem,and the optimization of solution needs to be improved.Firstly,a new sequential decision-ma-king framework for observation tasks is constructed in this paper.Based on this framework,an earth observation satellite can decide the observation task to be performed in real-time,without generating a plan in advance.Then,an observation task decision model based on attention mechanism,and the corresponding input feature representation method and model training method are designed.An observation task sequence algorithm based on attention neural network is proposed.Finally,based on a set of random data,the performance of the proposed algorithm,two deep learning algorithms and two heuristic online search algorithms are compared.Experimental results show that the response time of the proposed method is less than one-fifth of the existing deep learning algorithm,and the profit gap is much smaller than that of the heuristic search algorithms,which confirm the feasibility and effectiveness of our method.

Key words: Attention mechanism, Earth observation satellite, Recurrent neural network, Satellite onboard autonomous task planning, Sequential decision-making

中图分类号: 

  • TP391
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