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Mitigation of Receiver Biases to Derive Ionospheric SSR Corrections for Multi-GNSS PPP-RTK Under High Ionospheric Activity

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PPP-RTK is a GNSS-based method known for achieving centimeter-level accuracy in positioning. It relies on real-time precise products from the International GNSS Service (IGS) for orbits and clocks, utilizing S-system theory for parameter estimation. PPP-RTK finds applications in high-precision fields like navigation, surveying, and precision agriculture. However, ionospheric effects pose challenges, introducing signal delays that impact GNSS accuracy. These delays are related to Total Electron Contents (TEC) in the ionosphere. To address these challenges, ionospheric State Space Representation (SSR) corrections are used. These corrections provide a priori information for PPP-RTK users, accelerating solution convergence. Accurate ionospheric delay estimation is crucial, often relying on GNSS data from reference station networks. Yet, receiver hardware biases affect ionospheric delay estimates derived from GNSS processing, hindering reliable ionospheric models. Our proposed approach mitigates this issue by estimating and compensating for receiver hardware biases in Multi-GNSS ionospheric observables from PPP. CORS dual-frequency GNSS receivers estimate receiver biases, using ionospheric observables. Hourly coefficients representing Vertical Total Electron Content (VTEC) spatial behavior are estimated, along with receiver bias for each reference station. These biases, stable over short periods, are estimated in the previous day and can be applied for real-time ionospheric correction. This study evaluates receiver biases using Multi-GNSS ionospheric observables, addressing high ionospheric activity. Results show promising 1-2 TECU (~16-32 cm) slant ionospheric delays post-convergence, even during high ionospheric activity. These unbiased corrections have potential for accurate modeling.

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Proceedings of the 36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023, p. 2527-2538.

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