Deep-Learning-Enhanced Outlier Detection for Precise GNSS Positioning With Smartphones
Bibliographic record
Abstract
A robust outlier detection method is proposed for effective detection of smartphone Global Navigation Satellite System (GNSS) measurement outliers frequently caused by multipath and non-line-of-sight (NLOS) effects. The new method has been developed through a novel combination of a binary-tree-based solution separation (SS) test (SS-test) and the deep-learning techniques, which leverages the statistical testing’s outlier detection efficiency and the deep-learning’s proficiency in modeling complex relationships. First, the binary-tree-based SS-test identifies specific subsets of outlier-free measurements. Second, the network predicts three-dimensional (3D) positioning errors through those subsets. Third, we obtain an accurate positioning solution from the measurement subset with the smallest predicted errors. To validate the proposed approach, a vehicle-based field test was conducted with a smartphone. The testing results indicate that the proposed method has reduced the rate of large positioning errors by 79% and improved the positioning accuracy by 41% compared to the conventional methods without robust statistical testing.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".