Improving smartphone-based positioning accuracy with height constraint and application to pedestrian and vehicular positioning
Bibliographic record
Abstract
Since the release of Android version 7 in 2016, the smartphone users have had access to the raw global navigation satellite system (GNSS) measurements (i.e., pseudorange, carrier-phase, Doppler, and carrier-to-noise density ratio (C/N0)) through the new application programming interface (API) called android location (API level 24). This capability opens opportunities to apply different positioning techniques, ranging from absolute to differential techniques, to the smartphone observations. Precise point positioning (PPP) is a powerful method for conducting accurate real-time positioning using a single receiver, and it can be applied to the smartphone observations as well. Most PPP smartphone positioning studies have so far focused on utilizing the GNSS only observations obtained from the smartphone's API. However, incorporating additional information as constraints, such as height information, can enhance accuracy and overall stability. Although the vertical positioning accuracy of GNSS is generally lower than the horizontal accuracy, utilizing recorded height from the smartphone GNSS chipset can still be beneficial. This incorporation increases the degree of freedom and strengthens the geometry between the receiver and satellites. In this study, we assess the effectiveness of the uncombined PPP (UPPP) model in the presence of height constraints. We utilize both pedestrian walking and vehicular datasets collected by a dual-frequency Xiaomi Mi8 device to evaluate the effect of adding height constraint to PPP model. The results demonstrate an average improvement of 22% and 26% on the root-mean-square (RMS) of horizontal error and the 50th percentile error, respectively, when employing the height constraints UPPP model. Additionally, the findings indicated a decrease in PPP convergence time, further supporting the positive impact of incorporating height constraints.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".