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GNSS Site unmodeled error prediction based on machine learning

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Abstract

A number of studies have attempted to reduce the effect of observation errors on Global Navigation Satellite Systems positioning through empirical error models. However, due to the complex spatiotemporal characteristics of observation errors, the effects of these errors cannot be eliminated, resulting in the unmodeled error in the positioning results. Although many studies have been carried out on unmodeled error mitigation, most of which only focus on positioning model optimization and fail to make use of historical observation data. We explore the relationship between unmodeled error and observation features and develop a new data-driven approach based on machine learning. Historical observations of a specific station are used to predict the unmodeled error of a positioning model. Time–frequency analysis is used to evaluate the prediction results. The feasibility of applying the method to the precise point positioning (PPP) kinematic positioning is verified by using IGS station data. It is clear from the findings that the data-driven model can effectively predict the unmodeled errors in GNSS positioning, especially in low-frequency components. In addition, the influencing factors of the method are explored in detail and the relevant settings are recommended.

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Data availability

The raw/processed data required to reproduce the findings of this study are available from the International GNSS Service.

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Acknowledgements

This work is supported by the Natural Science Foundation of Jiangsu Province under Grant number BK20220367, University the Open Research Fund Program of LIESMARS under grant number 22P04, the National Natural Science Foundation of China under Grant number 42171417, 42271420, the Key Research and Development Program of Hubei Province under Grant number 2021BAA166, the Special Fund of Hubei Luojia Laboratory, the Special Research Fund of LIESMARS.

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Authors and Affiliations

  1. School of Geomatics Science and Technology, Nanjing Tech University, Nanjing, 210000, PR China

    Nan Shen

  2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, PR China

    Liang Chen, Lei Wang & Ruizhi Chen

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  1. Nan Shen
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  2. Liang Chen
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  3. Lei Wang
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Correspondence to Liang Chen.

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Shen, N., Chen, L., Wang, L. et al. GNSS Site unmodeled error prediction based on machine learning. GPS Solut 27, 77 (2023). https://doi.org/10.1007/s10291-023-01411-x

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