Jamming and Spoofing Detection and Classification Performance Under Hostile GNSS Environments
Bibliographic record
Abstract
Civilian Global Navigation Satellite Systems (GNSS) play a crucial role in critical infrastructure and safety-critical applications, where their low signal power and open descriptions make them vulnerable to threats such as jamming and spoofing. To address these major challenges and growing concerns, NovAtel’s OEM7 receivers are equipped with an advanced GNSS Resilience and Integrity Technology (GRIT) to identify and respond to GNSS threats effectively. This includes Interference Toolkit (ITK), Spoofing Detection Toolkit (SK) and Robust Dual-Antenna Receiver (RoDAR), which employ a range of countermeasures, from jamming detection and characterization to spoofing detection and mitigation, ensuring solution integrity and reliability. The newly developed Galileo Open Service Navigation Message Authentication (OSNMA) module also offers an additional layer of protection by checking for the authenticity of the navigation message for Galileo E1 signals. This paper evaluates the performance of NovAtel’s OEM7 receivers in detecting and mitigating jamming and spoofing using real event data. Effective jamming detection was achieved through spectrum monitoring across all GNSS bands. The effectiveness of GRIT’s anti-jamming and anti-spoofing technologies was demonstrated in advanced test cases. OSNMA results are discussed, highlighting its role as a complementary protection layer for enhanced GNSS security.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".