Performance Analysis of Precise Point Positioning Using Real-Time Orbit and Clock Products
Bibliographic record
Abstract
Abstract. The real-time availability of precise GPS satellite orbit and clock products has enabled the development of a novel positioning methodology known as precise point positioning (PPP). Based on the processing of un-differenced pseudorange and carrier phase observations from a single GPS receiver, positioning solutions with centimeter to decimeter accuracy can be attained globally. Such accuracy can currently be achieved only through differential processing of observations acquired simultaneously from at least two receiver stations. The potential impact of PPP on the positioning community is expected to be significant. It brings not only great flexibility to field operations but also reduces labor and equipment cost and simplifies operational logistics by eliminating the need for base stations. This paper will address issues related to precise point positioning and perform data analysis to assess the performance of different application solutions from PPP using real-time precise orbit and clock corrections. They include the discussion of an algorithm for un-differenced data processing, error source and mitigation, and critical elements related to real-time GPS orbit and clock products. Numerical results will be presented to show the positioning accuracy attained with datasets acquired from different environments using real-time precise orbit/clock products currently available. Features of a software package that has been developed at the University of Calgary for precise point positioning will also be described.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".