Mitigating high latitude ionospheric scintillation effects on GNSS Precise Point Positioning exploiting 1-s scintillation indices
Bibliographic record
Abstract
Abstract Ionospheric scintillation refers to rapid and random fluctuations in radio frequency signal intensity and phase, which occurs more frequently and severely at high latitudes under strong solar and geomagnetic activity. As one of the most challenging error sources affecting Global Navigation Satellite System (GNSS), scintillation can significantly degrade the performance of GNSS receivers, thereby leading to increased positioning errors. This study analyzes Global Positioning System (GPS) scintillation data recorded by two ionospheric scintillation monitoring receivers operational, respectively, in the Arctic and northern Canada during a geomagnetic storm in 2019. A novel approach is proposed to calculate 1-s scintillation indices. The 1-s receiver tracking error variances are then estimated, which are further used to mitigate the high latitude scintillation effects on GPS Precise Point Positioning. Results show that the 1-s scintillation indices can describe the signal fluctuations under scintillation more accurately. With the mitigation approach, the 3D positioning error is greatly reduced under scintillation analyzed in this study. Additionally, the 1-s tracking error variance achieves a better performance in scintillation mitigation compared with the previous approach which exploits 1-min tracking error variance estimated by the commonly used 1-min scintillation indices. This work is relevant for a better understanding of the high latitude scintillation effects on GNSS and is also beneficial for developing scintillation mitigation tools for 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".