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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".