Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
Bibliographic record
Abstract
To improve smartphone GNSS positioning performance using extra inequality information, an inequality constraint method was introduced and verified in this study. Firstly, the positioning model was reviewed and three constraint applications were derived from it, namely, vertical velocity, direction, and distance constraints. Secondly, we introduced an estimator based on the density function truncation method to solve the inequality constraint problem. Finally, the performance of the method was investigated using datasets from three smartphones, including a Huawei P30, a Huawei P40, and a Xiaomi MI8. The results indicate that the position and velocity accuracy can be improved in the up component using a vertical velocity constraint. The horizontal positioning accuracy was increased using a heading direction constraint with dynamic datasets. Numerically, the root mean square error (RMSE) improvement percentages were 16.77%, 14.57%, and 31.09% for HP40, HP30, and XMI8, respectively. Using an inter-smartphone distance constraint could enhance the horizontal positioning of all participating smartphones, with improvement percentages of 34.27%, 75.58%, and 23.66% for HP40, HP30, and XMI8, respectively, in the static dataset. Additionally, the improvement percentages were 15.90%, 5.55%, and 0.17% in dynamic datasets. In summary, this study demonstrates that utilizing inequality constraints can significantly improve smartphone GNSS positioning.
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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 | 0.004 |
| 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) | 0.002 | 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".