Improve the Security of GNSS Receivers Through Spoofing Mitigation
Why this work is in the frame
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Bibliographic record
Abstract
Spoofing attacks are one of the most dangerous threats for the application of the global navigation satellite system (GNSS), especially for autonomous driving and unmanned aerial vehicles. In this paper, we present a more robust spoofing mitigation algorithm based on subspace projection that is independent of the number of antennas and that can be utilized in single-antenna GNSS receivers. During a spoofing attack, authentic signals are contaminated by spoofing signals. We demonstrate that all spoofing signals can be eliminated by projecting the received signal onto the orthogonal null space of the spoofing signals. Moreover, two types of receiver structures are designed: a centralized structure that has the ability to suppress cross-correlation interference and a distributed structure with lower computational complexity and lower projection power losses. The proposed algorithm is verified by the Beidou B1I signals for improving the security of the receiver.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it