Gnss signal authenticity verification using carrier phase measurements with multiple receivers
Bibliographic record
Abstract
Structural interference signals can severely jeopardize the performance of GNSS receivers which may lead to serious consequences for scores of applications. This type of interference is designed to be very similar to the authentic GNSS signals; therefore, it is very difficult for a conventional receiver to discriminate them from genuine observations. This research focuses on the application of a carrier phase capable dual antenna receiver (or two spatially separated receivers) to allow authenticity verification and reliable measurements classification. Assuming that all counterfeit PRNs originate from the same source, the proposed method identifies fake measurements based on their time invariant carrier phase double differences. The proposed detection procedure is based on a combination of GLRT and graph theory formulated to classify counterfeit and authentic signals and to reduce the authenticity verification time. Simulations and real-data processing results verify that the proposed technique can successfully classify the authentic and counterfeit measurements within a few minutes. The real-world performance of this method has also been verified on carrier phase capable GNSS receivers. The theoretical analyses and processing results show that the performance of this technique improves as the observation interval or receiver antenna spacing increases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".