Multiantenna GNSS and Inertial Sensors/Odometer Coupling for Robust Vehicular Navigation
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
Location information is one of the most vital information required to achieve intelligence and context-awareness for Internet of Things applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on the use global navigation satellite system (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions. Radio frequency interference, either intentional or unintentional, has a direct impact on GNSS navigation performance related to observability and accuracy. In terms of security, spoofing is the major issue of concern. This paper focuses on multiantenna GNSS and inertial navigation system (INS)-odometer integration to improve robustness, security, and privacy of navigation solutions. Multiantenna GNSS provides robustness against different interference sources and integration with INS provides continuous navigation solutions during short-term signal outages. Performance of the proposed architecture is evaluated using different user scenarios in the presence of spoofing and interference signals in real vehicular environments.
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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 | 0.001 | 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