Detection of GPS spoofing through signal multipath signature analysis
Bibliographic record
Abstract
Spoofing that emulates authentic signals to coerce target receivers into false navigation solutions is a severe threat to many applications based on Global Positioning System (GPS). In order to forge a reasonable navigation solution, a spoofer generally has to mimic signals of several GPS satellites simultaneously. In view of the fact that GPS signals originated from a single transmitter would essentially experience an identical wireless channel to a receiver, a GPS spoofing detection scheme is proposed based on comparing the multipath signature of received signals. More specifically, the delay and gain ratio between multiple paths of a wireless channel that cannot be masked by spoofing signals are exploited. The delays between the strongest and other correlation peaks associated with each satellite are first evaluated. A correlation peak with the same delay for different satellites and closest to the strongest peak is then selected and its ratio to the strongest peak is calculated. With an identical wireless channel, the ratios corresponding to different satellites would be highly correlated. In contrast, ratios of authentic satellite signals that experience independent channels would be independent of each other. GPS spoofing can therefore be detected. The proposed design has been validated by simulations.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".