Estimating process noise variance of PPP-RTK corrections

a means for sensing the ionospheric time-variability

Journal article (2023)

Authors

Parvaneh Sadegh Nojehdeh University of Melbourne
Amir Khodabandeh University of Melbourne
Kourosh Khoshelham University of Melbourne
A. Amiri Simkooei Optical and Laser Remote Sensing - CEG
Research Group
Optical and Laser Remote Sensing (CEG) (TU Delft)
Copyright: © 2023 Parvaneh Sadegh Nojehdeh, Amir Khodabandeh, Kourosh Khoshelham, A. Amiri Simkooei
DOI
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https://doi.org/10.1007/s10291-023-01577-4
Correction latency Ionospheric time-variability Precise point positioning-real-time kinematic (PPP-RTK) Process noise variance-estimation
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Remote Sensing

Research Group

Optical and Laser Remote Sensing

Abstract

The provision of accurate ionospheric corrections in PPP-RTK enormously improves the performance of single-receiver user integer ambiguity resolution (IAR), thus enabling fast high precision positioning. While an external provider can disseminate such corrections to the user with a time delay, it is the task of the user to accurately time-predict the corrections so that they become applicable to the user positioning time. Accurate time prediction of the corrections requires a dynamic model in which the process noise of the corrections has to be correctly specified. In this contribution, we present an estimation method to determine the process noise variance of PPP-RTK corrections using single-receiver GNSS data. Our focus is on variance estimation of the first-order slant ionospheric delays, which allows one to analyze how the ionospheric process noise changes as a function of the solar activity, receiver local time, and receiver geographic latitude. By analyzing 11-year GNSS datasets, it is illustrated that estimates of the ionospheric process noise are strongly correlated with the solar flux index F10.7. These estimates also indicate a seasonal variation, with the highest level of variation observed during the spring and autumn equinoxes.