High precision kinematic positioning using single dual frequency GPS receiver
Bibliographic record
Abstract
Currently, high precision kinematic GPS positioning with centimetre level accuracy can only be carried out using differential GPS (DGPS) positioning techniques which require the deployment of base receiver stations. The requirement to deploy base receiver stations, however, spatially limits the operating range of the rover receiver to about 20 km from the base stations. As a result, it not only increases the operational costs of equipment and human resources but also significantly increases the logistical complexity for many applications such as land geodetic surveying and airborne geo-referencing and mapping. With the increased availability of precise GPS satellite orbit and clock data in real-time from the International GPS Service (IGS) and many other organizations, high precision kinematic positioning at centimetre to decimetre level is now possible using a single GPS receiver. Presented in this paper are the methods and algorithms that have been developed at the University of Calgary for high precision kinematic positioning using a single dual-frequency GPS receiver. Different from the conventional DGPS approach, the new system does not need a base station since the position determination is based on the processing of un-differenced GPS code and carrier phase observations. This eliminates the range limitation related to the conventional methods, resulting in instant advantages in field operations. A software system developed at the University of Calgary will also be described along with numerical results to demonstrate the obtainable positioning accuracy and its potential for various applications. 1.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".