PointPerfect Atmospheric Corrections Modeling
Gergis, Andrew (2026)
2026
Tietotekniikan DI-ohjelma - Master's Programme in Information Technology
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2026-07-01
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202607018075
https://urn.fi/URN:NBN:fi:tuni-202607018075
Tiivistelmä
The thesis examined atmospheric correction errors, in particular the ionospheric delay, for the PointPerfect PPP-RTK GNSS service as well as the reliability of the service quality indicator QI of u-blox receivers in different scenarios. For the modeling of the ionospheric residuals, after applying PointPerfect corrections, the dual-frequency observation models ionosphere-free and geometry-free were used. The investigations were performed with multi-GNSS data of the RINEX type as well as with precise products of various IGS stations in Germany and Italy. After downloading the RINEX and precise product files with a MATLAB-based processing stream the corrections, aligned to the SPARTN system, as well as an external GNSS solver were run. From the large datasets of observations, the ionospheric residuals and various quality measures were extracted.
The ionospheric residuals were analysed by error detection and by a fit of the errors to so-called parametric distributions (e.g. normal distribution or Student’s T-distribution). It has been proven that the ionospheric errors are of heavy-tailed nature and therefore are better described by a t-distribution than by a Gaussian distribution. Based on the results of the error analysis several variance-flooring strategies have been developed and tested. The variance-flooring strategies take into account the variances of the TEC-values as well as the variances of the service quality indicator QI and the variances of the mean values of QI of single receiver configurations. In addition, the 68th percentiles of the error distributions as well as the scales of the t-distribution have been used for the variance flooring. The developed variance-flooring strategies have been implemented into the receiver’s GNSS-firmware in the form of ionospheric constraint lookup tables. Large datasets of static as well as of dynamic test scenarios have been used to test the new variance-flooring strategies.
The test scenarios covered high as well as low ionospheric activities. The variance-flooring strategies based on the 68th percentiles as well as on the t-distribution-scales clearly have shown an improved ambiguity resolution robustness as well as a decreased number of wrong fixes of the receiver. The positioning accuracy in 2D as well as in 3D is found to be comparable to the one of the basic receiver without any variance flooring.
The ionospheric residuals were analysed by error detection and by a fit of the errors to so-called parametric distributions (e.g. normal distribution or Student’s T-distribution). It has been proven that the ionospheric errors are of heavy-tailed nature and therefore are better described by a t-distribution than by a Gaussian distribution. Based on the results of the error analysis several variance-flooring strategies have been developed and tested. The variance-flooring strategies take into account the variances of the TEC-values as well as the variances of the service quality indicator QI and the variances of the mean values of QI of single receiver configurations. In addition, the 68th percentiles of the error distributions as well as the scales of the t-distribution have been used for the variance flooring. The developed variance-flooring strategies have been implemented into the receiver’s GNSS-firmware in the form of ionospheric constraint lookup tables. Large datasets of static as well as of dynamic test scenarios have been used to test the new variance-flooring strategies.
The test scenarios covered high as well as low ionospheric activities. The variance-flooring strategies based on the 68th percentiles as well as on the t-distribution-scales clearly have shown an improved ambiguity resolution robustness as well as a decreased number of wrong fixes of the receiver. The positioning accuracy in 2D as well as in 3D is found to be comparable to the one of the basic receiver without any variance flooring.