Assessment of GNSS Performance and Error Bounding for SAIL III UAS Operations
Bibliographic record
Abstract
The growing use of UASs in complex operations, including Beyond Visual Line of Sight (BVLOS) operations and missions over populated areas, has increased the need for robust navigation integrity. Within this framework, a GNSS is often used as the primary source for positioning, but its reliability can be affected by various degradation sources, particularly in urban or constrained environments. This paper explores the implications of using GNSSs as an external service in SAIL III operations, with a focus on Operational Safety Objective (OSO) #13, defined in Specific Operations Risk Assessment (SORA) 2.5. A review of SORA 2.5 requirements is provided, followed by experiments involving GNSS data acquisitions in different environments using both high-end and mid-range receivers. Various performance indicators available from the receivers, such as the Dilution of Precision (DOP), Carrier-to-Noise Density Ratio (C/N0), estimated accuracy, and PLs, are examined to assess their ability to detect navigation degradations in real time. The results show that Protection Levels outperform the other indicators in detecting degradations under challenging conditions, highlighting the current limitations of GNSS-based navigation monitoring for specific category UAS operations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".