A Fast Approach to Satellite Range Rescheduling Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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A Fast Approach to Satellite Range Rescheduling Using Deep Reinforcement Learning

Published:2023-12 Issue:6 Volume:59 Page:9390-9403
ISSN:0018-9251
Container-title:IEEE Transactions on Aerospace and Electronic Systems
language:
Short-container-title:IEEE Trans. Aerosp. Electron. Syst.

Author:

Liang Jun¹^ORCID,Liu Jian-Ping¹^ORCID,Sun Qing¹^ORCID,Zhu Yue-He²^ORCID,Zhang Yi-Chuan¹^ORCID,Song Jian-Guo¹^ORCID,He Bo-Yong³^ORCID

Affiliation:

1. Xi'an Satellite Control Center, Xi'an, China

2. Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions, College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China

3. State Key Laboratory of Astronautic Dynamics, Xi'an, China

Funder

National Natural Science Foundation of China

Young Elite Scientists Sponsorship Program by the Cast of China Association for Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Aerospace Engineering

Link

http://xplorestaging.ieee.org/ielx7/7/10353051/10256124.pdf?arnumber=10256124

Reference32 articles.

1. LEO mega-constellation network: Networking technologies and state of the art;Chen;J. Commun.,2022

2. Analysis of Ground Station Network Resources for Giant Constellation TT&C Service

3. Automating Air Force Satellite control Network (AFSCN) scheduling

4. Scheduling and rescheduling with iterative repair

5. Acquisition, Doppler Tracking, and Positioning with Starlink LEO Satellites: First Results

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