GNSS spoofing detection in handheld receivers based on signal spatial correlation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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