A Hybrid Analytical-Machine Learning Approach for LEO Satellite Orbit Prediction
Author:
Affiliation:
1. University of California,Department of Electrical Engineering and Computer Science,Irvine,USA
2. University of California,Department of Mechanical and Aerospace Engineering,Irvine,USA
Funder
Office of Naval Research (ONR)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9841224/9841225/09841298.pdf?arnumber=9841298
Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. On the Fundamental Tracking Performance and Design Considerations of Radio Navigation;IEEE Journal on Selected Areas in Communications;2024-09
2. Ad Astra: Simultaneous Tracking and Navigation With Megaconstellation LEO Satellites;IEEE Aerospace and Electronic Systems Magazine;2024-09
3. An XGBoost-Based Method for Improved Orbit Prediction With an Orbit-Separate Modeling Strategy;IEEE Transactions on Aerospace and Electronic Systems;2024-08
4. A survey of machine learning techniques for improving Global Navigation Satellite Systems;EURASIP Journal on Advances in Signal Processing;2024-06-28
5. LSTM-based clock synchronization for satellite systems using inter-satellite ranging measurements;GPS Solutions;2024-06-23
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