Smartphone GNSS Lane-Level Navigation With Galileo HAS Corrections and an Iterative PPP Algorithm
Bibliographic record
Abstract
The last decade has seen substantial advancements in Internet of Things (IoT)-based transportation and smart city networks, fueling the growth of Global Navigation Satellite System (GNSS) industries and GNSS-enabled smartphones that deliver real-time, precise location-based services for mass-market applications. However, achieving decimeter-level smartphone positioning with GNSS processing techniques, such as precise point positioning (PPP) with real-time corrections in urban environments remains challenging due to the noisy and unstable nature of smartphone GNSS measurements. Key issues include low signal strength, high multipath effects, frequent cycle slips, and phase discontinuities, all of which degrade PPP accuracy and extend convergence times. To address these challenges, this study introduces a two-step clock bias preprocessing method to reduce Galileo High Accuracy Service outliers and biases. Additionally, an innovative iterative PPP algorithm integrated with a moving window approach is proposed to mitigate cycle slip false alarms and preserve ambiguity estimation continuity under difficult GNSS signal reception conditions. Validated through extensive vehicle experiments across eight datasets in diverse multipath environments, the proposed method demonstrates significant positioning accuracy improvements with four-constellation support. Results show a 95th percentile error and overall rms of 1.8 and 1.2 m, respectively, in horizontal positioning, with submeter lateral rms (0.8 m) and 99% lane-determination success rate in realistic driving scenarios. These findings indicate the potential of smartphone-based real-time PPP in enabling lane-level navigation, paving the way for next-generation IoT-integrated location 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) | 0.000 | 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".