Bibliographic record
Abstract
Nowadays, GNSS signal spoofing has become an increasingly concrete threat. Low-cost consumer electronics, software defined radios and ADS-B are some of the keys that made GNSS spoofing technology accessible. A function for detecting, or even mitigating, the occurrence of a spoofing attack on GNSS signals has therefore become mandatory for civilian equipment such as the SkyNaute. Thus, the new standard for GNSS aided inertial equipment (RTCA DO384) includes a dedicated appendix specifying how to claim and evaluate the performance of such a function. The solution patented by Safran consists of analyzing the statistical behavior of the shifts computed by the hybridization filter. Several new detectors based on shift magnitude and direction have been tested. Under normal conditions, this shift direction is random with a relatively large standard deviation. In the event of GNSS signals coherent spoofing, the shift direction becomes constant; the standard deviation tends towards zero. This is the principle of the detector implemented by Safran. This article aims to present these new detectors, their reaction under spoofing condition and the results of the first evaluation campaign. Further evaluation campaigns and development will be presented in the conclusion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".