Machine Learning based Multi-constellation Satellite Selection Algorithm in Urban Area
Bibliographic record
Abstract
GNSS positioning in urban canyons is vulnerable to signal outage, non-line-of-sight (NLOS) and multipath effects from high-rise buildings. While the increasing number of satellites from multi-constellation GNSS enhances signal availability and possibly positioning accuracy, the presence of degraded signals can significantly deteriorate performance. In general, removing available signals from position calculation may increase the level of dilution of precision (DOP). This study proposes machine learning-aided satellite selection techniques that can identify and exclude low-quality signals while maintaining the DOP level. Several signal quality indicators are employed as input features to train ensemble learning algorithms. Based on the predicted scores from the machine learning output, novel satellite selection criteria are employed to exclude low-quality signals. Experimental results using measurement based realworld urban datasets demonstrate that the proposed method effectively reduces positioning errors in both single and multiconstellation cases compared to least squares positioning using all available satellites. This work indicates the effectiveness of machine learning techniques in satellite navigation.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".