Single Frequency Ionosphere-free Precise Point Positioning: A Cross-correlation Problem?
Bibliographic record
Abstract
Single Frequency Ionosphere-free Precise Point Positioning: A Cross-correlation Problem? This research investigates the feasibility of applying the code and the ionosphere-free code and phase delay observables for single frequency Precise Point Positioning (PPP) processing. Two observation models were studied: the single frequency ionosphere-free code and phase delay, termed the quasi-phase observable, and the code and quasi-phase combination. When implementing the code and quasi-phase combination, the cross-correlation between the observables must be considered. However, the development of an appropriate weight matrix, which can adequately describe the noise characteristics of the single frequency code and quasi-phase observations, is not a trivial task. The noise in the code measurements is highly dependent on the effects of the ionosphere; while the quasi-phase measurements are basically free from the effects of the ionospheric error. Therefore, it is of interest to investigate whether the correlation between the two measurements can be neglected when the code measurements were re-introduced to constrain the initial parameters estimation and thereby improving the phase ambiguities initialization process. It is revealed that the assumed uncorrelated code and quasi-phase combination provided comparable if not better positioning precision than the quasi-phase measurement alone. The level of improvement in the estimated positions is between 1 - 18 cm RMS.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".