Multiantenna GNSS and Inertial Sensors/Odometer Coupling for Robust Vehicular Navigation
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".