A Resilient Reference Satellite Configuration for Smartphone RTK in Complex Environments
Bibliographic record
Abstract
Smartphones, being one of the most ubiquitous sensors in daily life, have the capability to receive Global Navigation Satellite System (GNSS) signals, thereby enabling them to provide location-based services (LBSs) for mass-market users. Spatial information is one of the vital components in intelligent transportation and internet of things applications, and transportation-related applications like lane-level navigation are among the most frequently used smartphone LBSs. Considering the high noise level of smartphone GNSS measurements in such applications, there is a risk of selecting a reference satellite with measurement outliers in relative positioning technology, which would therefore decrease positioning accuracy and reliability. To address this issue, this paper proposes a resilient reference satellite configuration in smartphone relative positioning, where two reference satellites are selected per frequency for each constellation, accompanied by an automatic switching strategy between single and dual-reference satellite configurations. The proposed method extends the observation equations with a second reference satellite, and is validated with 18 datasets collected in driving environments, and both theoretical analysis and positioning results demonstrate that the dual-reference satellite configuration outperforms conventional single-reference satellite strategies except in extremely harsh environments. When applying the resilient switch, the percentage of horizontal positioning errors within 4 meters is largely improved. Moreover, the 68th percentile horizontal positioning errors are reduced by ~3 decimeters compared to single reference satellite method, and the percentages of positioning errors within 1.0 and 1.5 meters are improved by 8% and 9%, respectively, indicating a higher capability and great potential of providing lane-level navigation with the proposed resilient reference satellite configuration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".