GNSS spoofing detection in handheld receivers based on signal spatial correlation
Bibliographic record
Abstract
Spoofing and jamming in the form of transmitting counterfeit location information and denying services are an emerging threat to GNSS receivers. In general, spoofing is a deliberate attack that aims to coerce GNSS receivers into generating false navigation solutions. The spoofing attack is potentially more hazardous than jamming since the target receiver is not aware of this threat and it is still providing position/navigation solutions which seem to be reliable. One major limitation of spoofers is that they are required to transmit several highly correlated GNSS signals simultaneously often from a single source in order to present a truthful navigation solution to the receiver. Different GNSS signals sourced from a single transmitter have essentially the same spatial signature, which as shown in this paper, can be utilized to discriminate the spoofing signals. In this paper a moving antenna is investigated to discriminate between the spatial signatures of the authentic and the spoofing signals based on monitoring the amplitude and Doppler correlation of the visible satellite signals. The effectiveness of this detection method is studied and verified based on a set of experiments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".