Deep Reinforcement Learning-Based Approach With Varying-Scale Generalization for the Earth Observation Satellite Scheduling Problem Considering Resource Consumptions and Supplements-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning-Based Approach With Varying-Scale Generalization for the Earth Observation Satellite Scheduling Problem Considering Resource Consumptions and Supplements

Published:2024-06 Issue:3 Volume:60 Page:2572-2585
ISSN:0018-9251
Container-title:IEEE Transactions on Aerospace and Electronic Systems
language:
Short-container-title:IEEE Trans. Aerosp. Electron. Syst.

Author:

Long Yaosong¹^ORCID,Shan Chengjun¹^ORCID,Shang Wei²^ORCID,Li Jin³,Wang Yulin³^ORCID

Affiliation:

1. School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, China

2. School of Mechanical Engineering, Hubei University of Technology, Wuhan, China

3. Institute of Remote-sensing Satellite, China Academy of Space Technology, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7/10552647/10475401.pdf?arnumber=10475401

Reference43 articles.

1. Coordinated Planning of Heterogeneous Earth Observation Resources

2. A Distributed Cooperative Dynamic Task Planning Algorithm for Multiple Satellites Based on Multi-agent Hybrid Learning

3. Planning and scheduling algorithms for the COSMO-SkyMed constellation

4. Estimating global ocean heat content from tidal magnetic satellite observations

5. A GA-SA Hybrid Planning Algorithm Combined with Improved Clustering for LEO Observation Satellite Missions

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