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Fast Fitting and Prediction of GEO Satellite Orbit Based on 11 Parameters of Intelsat
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Zhenghao ZHANG1, 2, 3, Yong HUANG2, 4, 5, *, Peng YANG2, 4, Xiaolin JIA1, 3, 6
Journal of Geodesy and Geodynamics | 2025, 45(5) : 488 - 493
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Journal of Geodesy and Geodynamics | 2025, 45(5): 488-493
Fast Fitting and Prediction of GEO Satellite Orbit Based on 11 Parameters of Intelsat
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Zhenghao ZHANG1, 2, 3, Yong HUANG2, 4, 5, *, Peng YANG2, 4, Xiaolin JIA1, 3, 6
Affiliations
  • 1 School of Geological Engineering and Geomatics, Chang'an University, 126 Yanta Road, Xi'an 710054, China
  • 2 Shanghai Astronomical Observatory, CAS, 80 Nandan Road, Shanghai 200030, China
  • 3 State Key Laboratory of Geo-Information Engineering, 1 Mid-Yanta Road, Xi'an 710054, China
  • 4 University of Chinese Academy of Sciences, A19 Yuquan Road, Beijing 100049, China
  • 5 Shanghai Key Laboratory of Space Navigation and Position Techniques, 80 Nandan Road, Shanghai 200030, China
  • 6 Xi'an Institute of Surveying and Mapping, 1 Mid-Yanta Road, Xi'an 710054, China

    • ZHANG Zhenghao, postgraduate, majorsinthe orbit determination of HEO satellite, E-mail:

About Author:

ZHANG Zhenghao, postgraduate, majorsinthe orbit determination of HEO satellite, E-mail:

Published: 2025-05-15 doi: 10.14075/j.jgg.2024.06.293
Outline
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The algorithm for solving the geostationary orbit(GEO) using 11 parameters published by the International Telecommunication Satellite Organization(Intelsat) has been extended and applied to the rapid fitting and forecasting of the orbits of four GEO satellites. Firstly, the inverse solution of the 11 parameters is derived in detail. Then, the dynamic prediction of GEO satellite orbits and their onboard GNSS data is simulated. Finally, the accuracy of the orbits with varying fitting and prediction arcs is evaluated. The results show that when fitting data for 6 h, the computation time is less than 0.9 s, and the fitting orbit accuracy is better than 100 m. When using the inverse 11 parameters from different fitting data arcs for prediction, the accuracy of the predicted orbits initially increases and then decreases as the fitting time increases. When the fitting time is 2 days, the prediction orbit accuracy is the highest, and the prediction orbit accuracy of 1 day is better than 1 200 m.

Intelsat  /  GEO  /  orbital fitting  /  orbital prediction
张政浩, 黄勇, 杨鹏, 贾小林. 基于国际通信卫星组织11参数的GEO卫星轨道快速拟合和预报. 大地测量与地球动力学, 2025 , 45 (5) : 488 -493 . DOI: 10.14075/j.jgg.2024.06.293
Zhenghao ZHANG, Yong HUANG, Peng YANG, Xiaolin JIA. Fast Fitting and Prediction of GEO Satellite Orbit Based on 11 Parameters of Intelsat[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (5) : 488 -493 . DOI: 10.14075/j.jgg.2024.06.293
Funding
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  • Preresearch Project on Civil Aerospace Technologies funded by the China National Space Administration(D010105)
  • Open Fund of State Key Laboratory of Geo-Information Engineering(SKLGIE2023-ZZ)
Appendix
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Year 2025 volume 45 Issue 5
25
10
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Article Info
doi: 10.14075/j.jgg.2024.06.293
  • Receive Date：2024-06-20
  • Online Date：2026-10-30
  • Published：2025-05-15
Article Data
Affiliations
History
  • Received：2024-06-20
Funding
Preresearch Project on Civil Aerospace Technologies funded by the China National Space Administration(D010105)
Open Fund of State Key Laboratory of Geo-Information Engineering(SKLGIE2023-ZZ)
Affiliations
    1 School of Geological Engineering and Geomatics, Chang'an University, 126 Yanta Road, Xi'an 710054, China
    2 Shanghai Astronomical Observatory, CAS, 80 Nandan Road, Shanghai 200030, China
    3 State Key Laboratory of Geo-Information Engineering, 1 Mid-Yanta Road, Xi'an 710054, China
    4 University of Chinese Academy of Sciences, A19 Yuquan Road, Beijing 100049, China
    5 Shanghai Key Laboratory of Space Navigation and Position Techniques, 80 Nandan Road, Shanghai 200030, China
    6 Xi'an Institute of Surveying and Mapping, 1 Mid-Yanta Road, Xi'an 710054, China
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https://castjournals.cast.org.cn/joweb/ddcl/EN/10.14075/j.jgg.2024.06.293
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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