WBMod assisted PLL GPS software receiver for mitigating scintillation affect in high latitude region
Bibliographic record
Abstract
Ionospheric scintillation occurs for transionospheric radio waves propagating through random ionospheric irregularities, which affect the phase and/or amplitude observations made by the receiver. Generally, the scintillation induces excess carrier phase jitter in the phase lock loop (PLL) of the GPS receiver, and strong scintillation can cause a conventional PLL (ATAN [arctangent method], constant bandwidth Bn = 10 Hz) to lose phase lock resulting in no GNSS signal available at that time from the satellite path(s) affected. A PLL with a larger bandwidth is one solution to mitigate this but at the expense of extra phase noise, and this may not be an optimal solution during weak scintillation conditions. This study uses a novel WBMod (Wide Band Modeling) assisted PLL for robustness of availability of GPS services with lower introduction of extra phase noise. At the initial stage, an optimal PLL bandwidth is predicted using WBMod to stabilize the PLL during strong phase scintillation. A FAB (Fast Adaptive Bandwidth) PLL is used to minimize the phase error. To investigate this approach, a realistic scintillated signal is produced using 50 Hz raw GPS signal observations (carrier phase and intensity) collected at Yellowknife (Yell, 64.48° N, -114.52° E) in a Matlab-based GPS software receiver.
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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 | 0.001 |
| 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) | 0.001 | 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".