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Wireless and Satellite Systems. 11th EAI International Conference, WiSATS 2020, Nanjing, China, September 17-18, 2020, Proceedings, Part II

Research Article

Data-Driven Approach for Satellite Onboard Observation Task Planning Based on Ensemble Learning

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  • @INPROCEEDINGS{10.1007/978-3-030-69072-4_15,
        author={Shuang Peng and Jiangjiang Wu and Chun Du and Hao Chen and Jun Li},
        title={Data-Driven Approach for Satellite Onboard Observation Task Planning Based on Ensemble Learning},
        proceedings={Wireless and Satellite Systems. 11th EAI International Conference, WiSATS 2020, Nanjing, China, September 17-18, 2020, Proceedings, Part II},
        proceedings_a={WISATS PART 2},
        year={2021},
        month={2},
        keywords={Satellite autonomy Observation Task Planning Data-driven onboard planning Machine learning Ensemble learning},
        doi={10.1007/978-3-030-69072-4_15}
    }
    
  • Shuang Peng
    Jiangjiang Wu
    Chun Du
    Hao Chen
    Jun Li
    Year: 2021
    Data-Driven Approach for Satellite Onboard Observation Task Planning Based on Ensemble Learning
    WISATS PART 2
    Springer
    DOI: 10.1007/978-3-030-69072-4_15
Shuang Peng1, Jiangjiang Wu1, Chun Du1, Hao Chen1,*, Jun Li1
  • 1: College of Electronic Science and Technology, National University of Defense Technology
*Contact email: hchen@nudt.edu.cn

Abstract

Onboard task planning can enhance the responsiveness of satellite to dynamic changes, which has attracted widespread attention. In this paper, the Satellite Onboard Observation Task Planning (SOOTP) problem is studied, and a data-driven onboard planning approach is proposed to decide the observation task to execute in real-time using machine learning techniques. In the approach, the satellite can learn how to make optimal decisions from the historical planning results. What is more, we design five types of features and employ three ensemble learning algorithms to solve the SOOTP. A comparison of the proposed method against two online searching algorithms indicates that the former has smaller profit gap and shorter response time, which verify the feasibility of our method.

Keywords
Satellite autonomy Observation Task Planning Data-driven onboard planning Machine learning Ensemble learning
Published
2021-02-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-69072-4_15
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