Investigating the Impact of Ionospheric Scintillation using a GPS Software Receiver
Bibliographic record
Abstract
Ionospheric scintillations cause RF signal amplitude fading and phase variations as GPS satellite signals pass through the ionosphere. This is a particular concern for GPS operations in high latitude regions, such as Canada, where scintillations are associated with strong aurora – effects which persist even during solar minimum. In general, scintillations can cause degraded receiver tracking performance and, in extreme cases, loss of navigation capabilities entirely. Such effects are an issue for reliable GPS operations in the northern United States and Canada. The University of Calgary currently operates the Canadian GPS Network for Ionosphere Monitoring (CANGIM), which makes scintillations observations at various latitudes in the auroral and sub-auroral regions. These measurements are used to characterize high-latitude scintillation effects, and develop models for assessing GPS receiver performance in the presence of such effects. The focus of this paper is a study of the effects of ionospheric scintillation on GPS signals, based on a GPS software receiver developed at University of Calgary. This study consists of several components: simulating ionospheric scintillation effects on L1 using an intermediate frequency (IF) GPS software signal simulator, investigating phase lock loop (PLL) performance under scintillation conditions using a software receiver, and developing improved tracking loop models to minimize phase errors and loss of signal lock during scintillation events. Results indicate that PLL performance is degraded for moderate to severe scintillations, with loss of lock occurring for narrow bandwidths. By employing a fast adaptive bandwidth approach, reliable signal tracking can be achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".