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S2L-RTK: temporal ionospheric modeling for RTK baselines varying from short to long

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Abstract

Real-time kinematic (RTK) has been widely used in mobile mapping. In a large-scale mobile mapping scene with long baseline, such as airborne gravimetry, the correlation of parameters between the base and rover stations is greatly weakened, especially since the ionospheric and the ambiguities parameters are difficult to be separated quickly. In this contribution, combined with the motion characteristics of the moving platform, a real-time differential positioning method constrained by ionospheric modeling in short-to-long baseline scenes (S2L-RTK) is proposed. First, in the short baseline case, the ionosphere was strongly constrained to obtain the fixed ambiguity solution, and then the ionospheric delay was extracted and modeled in real time. When the carrier is in the long baseline case, the ionospheric parameters are constrained by the ionospheric prediction, and then the fixed solution is obtained quickly. This repeated process of ionospheric modeling prediction and ambiguity fixing realizes a large-scale dynamic high-precision positioning with only a single base station setup, which can significantly reduce operating costs. Three different processing models are evaluated: the ionospheric-free model, the ionospheric-weighted model, and the new proposed S2L-RTK model. With airborne and vehicle experiments data processed, it is proved that the ambiguity fixing rate of S2L-RTK model can reach nearly 100% in open-sky scenes and 90% in complex urban scenes, and it can obtain centimeter-level positioning accuracy in both scenes, which is significantly better than the ionospheric-free and ionospheric-weighted models.

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The data used in this manuscript are available from the corresponding author upon request.

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Acknowledgements

This study is supported by the National Key Research and Development Program of China (Grant No. 2022YFB3903802), the National Natural Science Foundation of China (Grant No. 42104021), the Science and Technology Major Project of Hubei Province (Grant No. 2021AAA010), and the Special Fund of Hubei Luojia Laboratory (Grant No. 2201000038).

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Authors and Affiliations

  1. Hubei Luojia Laboratory, School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China

    Feng Zhu, Xi Chen, Wanke Liu & Xiaohong Zhang

  2. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China

    Feng Zhu

  3. Chinese Antarctic Center of Surveying and Mapping, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China

    Xiaohong Zhang

  4. GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China

    Liye Ma

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  1. Feng Zhu
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  2. Xi Chen
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  3. Liye Ma
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  4. Wanke Liu
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  5. Xiaohong Zhang
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Contributions

FZ devised the main conceptual ideas. FZ and XC performed the research, analyzed the data, and wrote the paper. LYM gave helpful advice on shaping the analysis, result, and manuscript. WKL and XHZ provided guidance for the development of the algorithm.

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Correspondence to Feng Zhu.

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Zhu, F., Chen, X., Ma, L. et al. S2L-RTK: temporal ionospheric modeling for RTK baselines varying from short to long. GPS Solut 27, 156 (2023). https://doi.org/10.1007/s10291-023-01505-6

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