Real-Time GPS/Galileo Precise Point Positioning Using NAVCAST Real-Time Corrections
Bibliographic record
Abstract
Real-time precise point positioning (PPP) is possible through the use of real-time precise satellite orbit and clock corrections, which are available through a number of organizations including the International GNSS Service (IGS) real-time service (IGS-RTS). Unfortunately, IGS-RTS is only available for the GPS and GLONASS constellations. In 2018, a new real-time service, NAVCAST, which provides real-time precise orbit and clock corrections for the GPS and Galileo constellations, was launched. In this research, the potential performance of real-time PPP which makes use of NAVCAST real-time corrections is analyzed using various static and kinematic datasets. In the static dataset, 24 hours of observations from eight IGS stations in Canada over three different days were utilized. The static results show that the contribution of Galileo satellites can improve the positioning accuracy, with 30%, 34%, and 31% in east, north, and up directions compared to the GPS-only counterparts. In addition, centimeter-level positioning accuracy in the horizontal direction and decimeter-level positioning accuracy in the vertical direction can be achieved by adding Galileo observations. In the kinematic dataset, a real vehicular test was conducted in urban and suburban combined areas. The real-time kinematic GPS/Galileo PPP solutions demonstrate an improvement of about 53%, 45%, and 70% in east, north, and up directions compared to the GPS-only counterparts. It is shown that the real-time GPS/Galileo PPP can achieve a sub-decimeter horizontal positioning accuracy and about meter-level vertical positioning accuracy through the use of NAVCAST real-time corrections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".