Computer Science ›› 2022, Vol. 49 ›› Issue (7): 242-247.doi: 10.11896/jsjkx.210500093

• Computer Network • Previous Articles     Next Articles

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).

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

CLC Number: 

  • TP391
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