Galileo IOV RTK positioning: standalone and combined with GPS
Bibliographic record
Abstract
Results are presented of real time kinematic (RTK) positioning based on carrier phase and code (pseudorange) observations of the four Galileo In-Orbit Validation (IOV) satellites, as they were in orbit and transmitting navigation data at the time of writing this article (2013). These Galileo data were collected by multi-GNSS receivers operated by Curtin University and as such this article is one of the first presenting results of short baseline ambiguity resolution and positioning based on Galileo IOV observations. The results demonstrate that integer ambiguity resolution based on the four IOV satellites needs fewer than three minutes when at least observables from three frequencies are used. Combined with data of four GPS satellites even instantaneous (single epoch) ambiguity resolution is demonstrated, using only two frequencies per constellation (i.e. E1+E5a & L1+L2). We also show that at locations with obstructed satellite visibility, such that positioning based on either GPS-only or Galileo-only becomes impossible or only in a very inaccurate way, combined Galileo&GPS positioning is feasible, within 10 min if one frequency of each constellation is used and only 2 min time-to-fix the ambiguities based on observations of two frequencies of each constellation. It is furthermore demonstrated that this results in positions with centimetre level accuracy in the horizontal plane and sub-decimetre accuracy in the vertical direction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".