Enhancing Smartphone Relative Positioning With Partial Wide-Lane Ambiguity Resolution: Path to Real-Time, Decimeter-Level Positioning in User Environments
Bibliographic record
Abstract
The ubiquity of smartphones catalyzes myriad smartphone-based Internet of Things (IoT) applications, amongst which smartphone positioning which utilizes global navigation satellite system (GNSS) observations to provide spatial information plays a crucial role. However, noisy smartphone GNSS measurements prevent decimeter-level positioning performance in user environments. Recovering the integer property of GNSS carrier phase measurement ambiguities shows great potential in achieving high-accuracy positioning solutions. However, inaccurate ambiguity estimates and short signal wavelengths are the main barriers to successful ambiguity resolution (AR). Therefore, a partial AR with wide-lane (WL) ambiguities and an automatic ambiguity hold strategy is proposed. Simulated results show that, for single-epoch WL AR, even with one WL ambiguity correctly fixed, the positioning solution can be improved by 3 cm. With actual static and kinematic datasets, the proposed algorithm is evaluated and validated. Static results show an improvement of 83% in horizontal position when the WL AR approach is applied, and positioning accuracies can reach 6.8, 2.9, and 11.5 cm in the E, N, and U direction components, respectively. For kinematic data collected in highly variable realistic driving environments, the time series of positioning errors of WL AR solutions exhibit less variation than float solutions. And with fixed WL ambiguities, solutions can be improved to varying degrees, ranging from several centimeters to up to 8 dm depending on the environment. The largest improvement of 8 dm is observed for 95th percentile horizontal positioning errors under a suburban environment.
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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 | 0.001 |
| 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".