Smartphone PPP-RTK positioning with Galileo high accuracy service and atmospheric correction from single reference station
Bibliographic record
Abstract
The widespread adoption of smartphones has spurred advancements in navigation technology. Traditional positioning methods, such as precise point positioning (PPP) and real-time kinematic (RTK), face limitations including long convergence times and high data rate requirements, making them unsuitable for smartphone applications. The PPP-RTK approach offers faster ambiguity resolution by leveraging precise atmospheric corrections, which is expensive to install and maintain from Continuously Operating Reference Stations (CORS) or multiple reference stations. Although PPP-RTK from single reference station is developed, satellite orbital/clock and code bias corrections from ground-based CORS stations and data transmissions are still required. While using the satellite-based Galileo high accuracy service (HAS) can achieve high-accuracy positioning for electronic devices including smartphones, few studies have studied HAS corrections to assess its effectiveness. In this study, we propose a smartphone precise positioning method from PPP-RTK with HAS and single reference station. Moreover, due to the high noise level of smartphone observations, a timedifferenced carrier-phase (TDCP) model coupled with solution separation (SS) testing is used to detect cycle slips. To validate the proposed method, experiments are carried out in static and kinematic modes. The results show the proposed method can achieve convergence in both static and kinematic condition around 2 minutes with ambiguity resolution, achieving an accuracy of 0.411 m horizontally and 0.754 m in three-dimension in kinematic mode. It suggests that utilizing current HAS products with GPS and Galileo smartphone PPP-RTK methodology can enable decimeter-level positioning precision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".