Enhancing Smartphone Positioning with Galileo HAS Corrections and an Environmentally-Aware PPP/IMU Approach
Bibliographic record
Abstract
With the increasing demand for high-precision positioning in smartphone-based navigation applications, such as smart transportation, autonomous driving, and urban mobility, achieving real-time lane-level accuracy remains a significant challenge due to noisy Global Navigation Satellite System (GNSS) observations, frequent signal outages, and cycle slip false alarms. To address these issues, this study extends the iterative Precise Point Positioning (PPP) algorithm by integrating an environmentally-aware approach, which adaptively selects ambiguity candidates based on historical data and environments to mitigate errors in ambiguity estimation. Additionally, this study incorporates multi-constellation GNSS processing by integrating HAS corrections for GPS and Galileo (GE) constellations with broadcast ephemeris for GLONASS and BeiDou (RC) constellations, enhancing observation redundancy and positioning stability. To further improve GNSS outage mitigation, smart-phone Inertial Measurement Unit (IMU) data are fused with GNSS observations to bridge signal interruptions. The proposed approach is validated through three real-world vehicle datasets. Results demonstrate that the environmentally-aware iterative PPP algorithm with multi-constellation (GREC) support reduces overall horizontal rms from 2.3 m to 1.4 m for dataset 1 and achieves sub-meter accuracy for dataset 2. By further integrating smartphone IMU data, maximum positioning errors in severe GNSS outages are reduced from 10.9 m to 2.9 m, with overall horizontal rms improving from 1.4 m to 1.3 m for dataset 3. These findings highlight the potential of real-time smartphone-based PPP in achieving sub-meter lane-level navigation, paving the way for next-generation smartphone-based positioning services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".