Integer Ambiguity Resolution to Improve Accuracy and Convergence of PPP-Inferred Troposphere Estimates
Bibliographic record
Abstract
Ambiguity resolution in Precise Point Positioning (PPP) is a challenging research topic within the GPS community in recent years. A decoupled clock model has been proposed by Natural Resource Canada (NRCan) and various researches have been conducted to investigate the model’s potential to improve positioning accuracy, reduce position convergence time and recover the integer property of un-differenced carrier phase ambiguity parameters. To date, no work however is available about the model’s potential to improve the estimation of tropospheric parameter. This paper investigates the effect of PPP ambiguity resolution on the troposphere estimation with respect to convergence time and obtainable accuracy using the decoupled clock model and associated corrections. Long initialization time is usually needed for PPP-inferred troposphere estimation. For example, ~4 hours are necessary to reach the convergence for a static GPS receiver with an average of 7 visible satellites, a PDOP value of ~3 and a data sampling rate 30 seconds. After applying the PPP decoupled clock model and products, this paper demonstrates that all test dataset at a sampling rate of 1 Hz can converge to cm-level accuracy within one hour for the horizontal coordinates. To assess the accuracy improvement for the tropospheric parameters, both the float and fixed troposphere estimates are compared to two external references. The first reference solution is the IGS final troposphere product based on the traditional PPP model. The second reference solution is the Constellation Observing System for Meteorology Ionosphere & Climate (COSMIC) radio occultation (RO) observation based on atmospheric profiles along the signal travel path. A comparison among ten co-located ground-based GPS and space-based RO troposphere zenith path delays shows that the bias in float troposphere estimates can be largely reduced by ambiguity resolution, from -0.0037 to 0.001 m when compared to the IGS troposphere product and from -0.0038 to 0.0009 m when compared to the COSMIC RO. The accuracy (RMS) improvement of tropospheric parameters by ambiguity resolution is 33.3% when compared to the IGS products, and 44.3% when compared to the COSMIC RO.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".