Real-time high-precision joint orbit determination of GPS and LEO using SRIF

Abstract Global navigation satellite system (GNSS) is currently widely used in fields such as navigation, positioning and timing. High-performance real-time orbit products are the foundation of its applications. To improve the performance of real-time orbit, real-time precise orbit determination (RT-POD) based on filtering has become a popular research area. However, existing research relies solely on ground observations. There are very limited studies have evaluated the potential contribution of LEO satellites to GNSS RT-POD based on filtering, or have the scenarios in which globally distributed stations are unavailable due to financial or politically reasons been considered. The feasibility of RT-POD based on filtering under these scenarios has not been studied. Therefore, the square root information filter is employed by this study for real-time joint orbit determination (RTJOD) of GPS and LEO satellites. Based on observations from eight existing scientific LEO satellites, a detailed study on RTJOD was conducted under two scenarios: one using observations from 100 global stations and the other using observations from 9 regional stations in Australia. The results show that, with 100 global stations, incorporating LEO observations can significantly improve the convergence performance and accuracy for GPS satellites. The convergence times in the along-track, cross-track components are reduced from 3.5, 5.8 h to 0.9, 1.0 h, respectively, while the radial component is kept at 10.3 h. The accuracy improves from 5.8, 3.6, and 2.8–4.0 cm, 2.5 cm, and 2.5 cm. Additionally, the ambiguity resolution (AR) performance is significantly enhanced. The time required to achieve a 90% narrow-lane ambiguity fixing rate is reduced from 4.9 to 0.7 h. After AR, the orbit accuracy further improves to 3.1, 2.3, and 2.4 cm. In the case of the 9 regional stations in Australia, after incorporating LEO, the orbit accuracy of the float solution after convergence is comparable to that of the 100 global stations without LEO, with accuracies of 6.0, 4.8, and 2.9 cm in the three components. It is important to note that, due to insufficient observations in this case, AR does not result in any further improvement in accuracy. In addition, LEO can achieve orbit determination accuracy better than 5 cm within a short time in both station distribution scenarios. This ensures that RTJOD enables LEO and GPS to generate high-precision real-time orbits simultaneously. Finally, the processing time for each epoch in all scenarios is less than 5 s, ensuring that the GPS and LEO RTJOD can provide timely orbit updates.

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Publication Details

Journal
Measurement Science and Technology
Published
2025-04-29
DOI
https://doi.org/10.1088/1361-6501/add200
Citations
3
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
14.98

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article

Real-time high-precision joint orbit determination of GPS and LEO using SRIF

Wen Lai, Haonan She, Wei Xie, Le Wang et al.
3 citations
Measurement Science and Technology
Inertial Sensor and Navigation
14.98
article

Real-time high-precision joint orbit determination of GPS and LEO using SRIF

Wen Lai, Haonan She, Wei Xie, Le Wang, Guanwen Huang, Qining Wang, Shichao Xie
article en
3 citations

Abstract

Abstract Global navigation satellite system (GNSS) is currently widely used in fields such as navigation, positioning and timing. High-performance real-time orbit products are the foundation of its applications. To improve the performance of real-time orbit, real-time precise orbit determination (RT-POD) based on filtering has become a popular research area. However, existing research relies solely on ground observations. There are very limited studies have evaluated the potential contribution of LEO satellites to GNSS RT-POD based on filtering, or have the scenarios in which globally distributed stations are unavailable due to financial or politically reasons been considered. The feasibility of RT-POD based on filtering under these scenarios has not been studied. Therefore, the square root information filter is employed by this study for real-time joint orbit determination (RTJOD) of GPS and LEO satellites. Based on observations from eight existing scientific LEO satellites, a detailed study on RTJOD was conducted under two scenarios: one using observations from 100 global stations and the other using observations from 9 regional stations in Australia. The results show that, with 100 global stations, incorporating LEO observations can significantly improve the convergence performance and accuracy for GPS satellites. The convergence times in the along-track, cross-track components are reduced from 3.5, 5.8 h to 0.9, 1.0 h, respectively, while the radial component is kept at 10.3 h. The accuracy improves from 5.8, 3.6, and 2.8–4.0 cm, 2.5 cm, and 2.5 cm. Additionally, the ambiguity resolution (AR) performance is significantly enhanced. The time required to achieve a 90% narrow-lane ambiguity fixing rate is reduced from 4.9 to 0.7 h. After AR, the orbit accuracy further improves to 3.1, 2.3, and 2.4 cm. In the case of the 9 regional stations in Australia, after incorporating LEO, the orbit accuracy of the float solution after convergence is comparable to that of the 100 global stations without LEO, with accuracies of 6.0, 4.8, and 2.9 cm in the three components. It is important to note that, due to insufficient observations in this case, AR does not result in any further improvement in accuracy. In addition, LEO can achieve orbit determination accuracy better than 5 cm within a short time in both station distribution scenarios. This ensures that RTJOD enables LEO and GPS to generate high-precision real-time orbits simultaneously. Finally, the processing time for each epoch in all scenarios is less than 5 s, ensuring that the GPS and LEO RTJOD can provide timely orbit updates.

Measurement Science and TechnologyVol. 36(5)
Chang'an University (CN), National Time Service Center (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 2%
Inertial Sensor and Navigation
14.98
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