Fault Detection and Correction Using Observation Domain Optimization for GNSS Applications
Bibliographic record
Abstract
Global Navigation Satellite System (GNSS) is ubiquitously used and integrated into a variety of applications that require accurate and reliable positioning, navigation, and timing (PNT). The rapid growth in research and development into autonomous and semi-autonomous land and aerial vehicle platforms in recent years has redefined industry standards for accurate and reliable PNT. To ensure the integrity of a PNT solution, effective fault detection and exclusion/correction (FDE/C) is needed. Least-squares residuals (LSR) and solution separation (SS) are two well-known receiver autonomous integrity monitoring (RAIM) methods. LSR is computationally efficient but is not applicable, nor is theoretically correct, in scenarios where multiple faulty observations are present. While SS is effective for detecting and isolating multiple faulty observations at a time, it has high computational complexity, hence not suitable for most real-time applications. Other existing fault classifier methods lack the industry required performance due to either data generalization and/or high computational complexity. A novel scalable multi-fault detection and correction method is presented here. As demonstrated by our analysis and test results based on both simulated and real data, the proposed method outperforms LSR providing a more accurate PNT solution and is 80% more computationally efficient than SS under nominal multi-constellation scenarios with 30 or more satellites used in the position estimation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".