A broadcast ephemeris design of LEO navigation augmentation satellites based on the integration-type ephemeris model

Author:

Meng LingdongORCID,Chen Junping,Wang Jiexian,Zhang Yize

Abstract

Abstract Low Earth orbit (LEO) satellites are a promising type of navigation augmentation satellite for current global navigation satellite systems. Aiming at the navigation function, an effective broadcast ephemeris model needs to be designed for LEO satellites. An enhanced integration-type broadcast ephemeris model is proposed in this study. First, the short-term periodical variation characteristics of LEOs’ accelerations in the Earth-centered Earth-fixed coordinate system are analyzed. The Chebyshev polynomials and harmonic functions are then applied to represent the variation perturbation of accelerations. Tests using simulated and real data from LEO satellites at altitudes from 600 to 1400 km are conducted to evaluate the fit accuracy of the proposed models in terms of arc length, integration method, integration step length, orbital altitude, inclination, eccentricity, etc. The fit accuracy is dramatically improved compared to that of the current GLONASS integration-type broadcast model, where fit errors less than 10 cm are achieved with an arc length of 20 min.

Funder

Key R&D Program of Guangdong province

National Key R&D Program of China

National Natural Science Foundation of China

Key Program of Special Development funds of Zhangjiang National Innovation Demonstration Zone

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Cited by 3 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. Enhancing ephemeris accuracy in low earth orbit: a Chebyshev polynomial fitting approach during earth's shadow period;Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023);2024-01-19

2. An optimal design of the broadcast ephemeris for LEO navigation augmentation systems;Geo-spatial Information Science;2022-01-02

3. Computing Offloading-based Task Scheduling for Space-based Cloud-fog Networks;2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT);2021-10

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