Double Chi-Squared Distributions-Based Advanced RAIM for Air Transportation
Bibliographic record
Abstract
Safety-critical dual-frequency and multi-constellation global navigation satellite system (GNSS) appli- cations are driving the development of advanced receiver autonomous integrity monitoring (ARAIM) to handle the effects of constellation faults, multi-satellite faults and nominal satellite biases. A Chi-squared residual-based ARAIM significantly differing from the traditional solution separation (SS) based ARAIM in principle is proposed for air transportation in this work. The proposed ARAIM expresses the integrity risk of each hypothesis as the product of the cumulative distribution functions of two Chi-squared distributions and the hypothesis occurrence probability. Based on the equivalence between the normalized solution separation and the residual Chi-square separation, an upper bound in terms of the worst Chi-squared distribution noncentrality parameter induced by GNSS faults is established for the integrity risk of each hypothesis. The log-concavity of the cumulative distribution function of Chi-squared distribution ensures that the protection level computation of the proposed ARAIM is theoretically convergent and conservative. A comparative analysis of the proposed ARAIM and SS-based ARAIM in civil aviation scenarios indicates that the proposed ARAIM can offer a competitive protection level.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".