Preliminary Analysis of GNSS Radio Frequency Interference Events Detected in Canada and Impacts on GNSS Based Applications
Bibliographic record
Abstract
Global Navigation Satellite Systems (GNSSs) transmit signals from space to Earth, enabling the determination of position, navigation and timing (PNT) information. National defence uses, safety-of-life applications and critical infrastructure (CI) sectors are just some of the areas that rely on PNT information provided by GNSS to improve safety and security, enable greater functionality, and increase productivity. PNT information has also become a fundamental enabler for many day-to-day applications ranging from the provision of directions while driving to fitness statistics on smartwatches to precision farming. This paper presents the findings of an in-depth analysis conducted on GNSS interference data collected from a specific site in Canada. The primary focus of this study was to examine the frequency, type, and severity of interference events observed at the site. The analysis provides valuable insights into the use of detectors, the nature of interference encountered, their potential sources, and their impact on the site's operations. Furthermore, this paper presents GNSS receiver parameters that can be utilized for automatic interference detection, along with technical recommendations for future detection algorithms. The detectors at the site have proven effective in detecting various types of interference including narrow band, chirp and single tone. The interference has a significant effect on C/N_0, number of satellites tracked, receiver noise, etc. and a loss of lock of signals can be encountered.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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".