Performance of Network RTK Using Fixed and Float Ambiguities
Bibliographic record
Abstract
Real time kinematic differential GPS positioning (RTK) at the cm–level is generally carried out using real time data from only one reference receiver. Current research activities have shown, however, that the performance of RTK positioning can be improved considerably over longer baselines if differential phase corrections are generated based on a network of reference stations. These analyses show positioning accuracies better than 10 cm. This level of accuracy has been obtained only when L1 phase ambiguities for the baselines between the stations in the reference network have been solved to fixed integer values. This paper focuses on the use of float ambiguities for the reference station vectors in the network. Float ambiguities are being investigated since it can be difficult to resolve the integer ambiguities efficiently and reliably in real time over a network with baseline lengths of typically 40 to 200 km. Based on GPS data from a number of reference stations the ambiguities for the baselines in the reference network are determined as both fixed and float values. Data from the reference stations are used along with either the fixed or float ambiguities to generate phase corrections by utilising a new method developed at the University of Calgary. Positioning of a user receiver is carried out employing the corrected phase data and conventional GPS positioning software. Various scenarios are evaluated whereby the ambiguities are solved to either fixed or float numbers, and using both single and dual frequency GPS data. The results are analysed emphasising on the positioning accuracy, and it is concluded that the accuracy over 24 hours is of about the same level if using fixed or float ambiguities. Finally the method is evaluated for eventual real time use.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".