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ROTI-based statistical regression models for GNSS precise point positioning errors associated with ionospheric plasma irregularities

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Abstract

Global Navigation Satellite System (GNSS) signals are susceptible to ionospheric plasma irregularities and associated scintillations, causing large deviations in the positioning solutions. This study aims to develop statistical regression models to estimate kinematic three-dimensional (3D) precise point positioning errors associated with ionospheric plasma irregularities based on the Rate Of Total electron content Index (ROTI). By assuming that the positioning errors follow the Laplace distribution, we perform nonlinear regression using the Levenberg–Marquardt algorithm on a collection of experimental data from 700 + Trimble receivers deployed in the NOAA Continuously Operating Reference Stations (CORS) Network. Three ROTI-based regression models are identified by curve fitting with nonlinear functions, i.e., third-degree polynomial (Poly3), two-term exponential (Exp2) and two-term power (Power2) models. A goodness-of-fit test suggests the models fit well into the relationship between ROTI and the 3D positioning errors with the adjusted coefficient of determination above 0.97. The regression models are subsequently employed to predict the 3D positioning errors with a given set of ROTI. Evaluation analysis using the observations from four CORS networks across different geographical regions indicate that the Exp2 model demonstrates encouraging prediction performance, with bias and root mean square error within − 0.14 m and 0.34 m, respectively, and the correct prediction ratio consistently surpasses 60.3%. The ROTI-based regression models have great potential in predictions of the degradation in GNSS positioning due to ionospheric space weather effects.

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Data availability

The raw GNSS datasets are available from the NOAA's National Geodetic Survey (https://geodesy.noaa.gov/corsdata/), Brazilian Institute of Geography and Statistics (https://www.ibge.gov.br/en/geosciences/geodetic-positioning/geodetic-networks), EPN Central Bureau (https://epncb.oma.be/) and the data center of the Crustal Movement Observation Network of China. The IMF, solar wind, and geomagnetic index data are available from the NASA Space Physics Data Facility OMNIWeb data server (https://omniweb.gsfc.nasa.gov/) and the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de/en/section/geomagnetism/data-products-services/). The RTKLIB (version 2.4.3 b34) can be accessed at this link (https://rtklib.com/).

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (42274027, 42225401), the Scientific and Technological Innovation Plan from Shanghai Science and Technology Committee (21511103902, 22511103003), the industrial Collaborative Innovation Project (Technology) of Shanghai Municipality (XTCX-KJ-2023-35, XTCX-KJ-2022-09), and the Fundamental Research Funds for the Central Universities.

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  1. College of Surveying and Geo-Informatics, Tongji University, Shanghai, China

    Haoyang Jia, Zhe Yang & Bofeng Li

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  1. Haoyang Jia
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  2. Zhe Yang
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  3. Bofeng Li
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Contributions

Z.Y. contributed to initial idea of the research. H.Y.J. conducted regression modelling and data analysis. H.Y.J wrote the manuscript text. Z.Y and B.F.L. revised the writing. All authors reviewed the manuscript.

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Correspondence to Zhe Yang.

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Jia, H., Yang, Z. & Li, B. ROTI-based statistical regression models for GNSS precise point positioning errors associated with ionospheric plasma irregularities. GPS Solut 28, 105 (2024). https://doi.org/10.1007/s10291-024-01648-0

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