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
[1]DU Y,WANG T,XIN B,et al.A data-driven parallel scheduling approach for multiple agile earth observation satellites[J].IEEE Transactions on Evolutionary Computation,2020,24(4):679-693.
[2]DENG M,LIU B,LI S,et al.A two-phase coordinated planning approach for heterogeneous earth-observation resources to monitor area targets[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2020,99:1-16.
[3]LIU M,ZHANG Q.Remote Sensing Data Transmission Mechanism Based on Distributed Cluster Architecture[J].Computer Engineering,2021,47(10):180-185.
[4]XIANG S,CHEN Y G,LI G L,et al.Review on satellite autonomous and collaborative task scheduling planning[J].Acta Automatica Sinica,2019,45(2):252-264.
[5]CHIEN S,SHERWOOD R,BURL M,et al.A demonstration of robust planning and scheduling in the techsat-21 autonomous sciencecraft constellation [J].Ear Nose&Throat Journal,2014,86(8):506-511.
[6]CHIEN S,RABIDEAU G,TRAN D,et al.Scheduling sciencecampaigns for the rosetta mission:a preliminary report [C]//International Workshop on Planning and Scheduling for Space.2013:1-8.
[7]BEAUMET G,VERFAILLIE G,CHARMEAU M.Feasibilityof autonomous decision making on board an agile earth-obser-ving satellite [J].Computational Intelligence,2011,27(1):123-139.
[8]LIU S,CHEN Y,XING L,et al.Time-dependent autonomous task planning of agile imaging satellites[J].Journal of Intelligent & Fuzzy Systems,2016,31(3):1365-1375.
[9]CHU X,CHEN Y,TAN Y.An anytime branch and bound algorithm for agile earth observation satellite onboard scheduling [J].Advances in Space Research,2017,60(9):2077-2090.
[10]LI G,XING L,CHEN Y.A hybrid online scheduling mechanism with revision and progressive techniques for autonomous Earth observation satellite [J].Acta Astronautica,2017,140(1):308-321.
[11]SU J M,LIU H F,XIANG F T,et al.Survey of Interpretation Methods for Deep Neural Networks[J].Computer Engineering,2020,46(9):1-15.
[12]WANG H J,YANG Z,ZHOU W G,et al.Online scheduling of image satellites based on neural networks and deep reinforcement learning [J].Chinese Journal of Aeronautics,2019,32(4):1011-1019.
[13]LI C,CAUSMAECKER P D,CHEN Y W.Data-driven onboard scheduling for an autonomous observation satellite[C]//Twenty-Seventh International Joint Conference on Artificial Intelligence.2018:5773-5774.
[14]LU J,CHEN Y,HE R.A learning-based approach for agile sate-llite onboard scheduling[J].IEEE Access,2020,99(8):16941-16952.
[15]ZHAO X,WANG Z,ZHENG G.Two-phase neural combinato-rial optimization with reinforcement learning for agile satellite scheduling[J].Journal of Aerospace Information Systems,2020,17(7):1-12.
[16]PENG S,CHEN H,DU C,et al.Onboard observation task planning for an autonomous earth observation satellite using long short-term memory[J].IEEE Access,2018,6(1):65118-65129.
[17]BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[C]//International Conference on Learning Representations.2015:1-15.
[18]SUTSKEVER I,VINYALS O,LE Q V.Sequence to sequencelearning with neural networks[C]//27th International Confe-rence on Neural Information Processing Systems.2014:3104-3112.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[3] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[4] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[5] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[6] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[7] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[8] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[9] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[10] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[11] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
[12] XIONG Luo-geng, ZHENG Shang, ZOU Hai-tao, YU Hua-long, GAO Shang. Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism [J]. Computer Science, 2022, 49(7): 212-219.
[13] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[14] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
[15] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!