Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling-Reference-Cited by-同舟云学术

Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling

Published:2024-04 Issue:4 Volume:54 Page:2576-2589
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Song Yanjie¹^ORCID,Ou Junwei²^ORCID,Pedrycz Witold³^ORCID,Suganthan Ponnuthurai Nagaratnam⁴^ORCID,Wang Xinwei⁵^ORCID,Xing Lining¹^ORCID,Zhang Yue⁶^ORCID

Affiliation:

1. School of Electronic Engineering, Xidian University, Xi’an, China

2. Department of Computer Science and the Cyberspace Security College, Xiangtan University, Xiangtan, China

3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

4. KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar

5. Department of Cognitive Robotics, TU Delft, Delft, CD, The Netherlands

6. School of Reliability and Systems Engineering, Beihang University, Beijing, China

Funder

Science and Technology Innovation Team of Shaanxi Province

Special Project in Major Fields of Guangdong Universities

Hunan Key Laboratory of Intelligent Decision-Making Technology for Emergency Management

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221021/10474538/10399872.pdf?arnumber=10399872

Reference44 articles.

1. Robust Earth Observation Satellite Scheduling With Uncertainty of Cloud Coverage

2. Optimisation problems and resolution methods in satellite scheduling and space-craft operation: a survey

3. Simulated Annealing-Based Heuristic for Multiple Agile Satellites Scheduling Under Cloud Coverage Uncertainty

4. The Earth-Observing Satellite Constellation: A review from a meteorological perspective of a complex, interconnected global system with extensive applications

5. Historical background and current developments for mapping burned area from satellite Earth observation

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