Detection and De-weighting of Multipath-affected Measurements in a GPS/Galileo Combined Solution
Bibliographic record
Abstract
Multipath is a major error source for Global Navigation Satellite Systems (GNSS). Different multipath countermeasure techniques have been investigated in the literature, of which detection and exclusion (or de-weighting) of affected measurements is receiving significant attention. Thanks to the development of multi-GNSS constellation receivers, this approach is increasingly effective as measurement redundancy is now sufficiently large to detect and exclude (or de-weight) faulty measurements prior to being used in the navigation solution. Given the benefits of GNSS measurement monitoring in detecting multiple signal failures, this paper focuses on measurement level monitoring techniques to develop effective mechanisms for detection and de-weighting of the signals distorted by multipath. The combination of GPS and Galileo signals is investigated under height-constrained environments to increase measurement redundancy and observability, and consequently improve the performance of detection and de-weighting solutions. Results obtained for a real static multipath scenario show up to 60 percent horizontal improvement for different positioning approaches.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".