Research Article
Data-Driven Approach for Satellite Onboard Observation Task Planning Based on Ensemble Learning
@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
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.
