Bibliographic record
Abstract
This research paper presents a sensor fusion methodology aimed at improving the accuracy and reliability of navigation systems in civil and general aviation, particularly during approach and landing. The objective is to provide sufficient accuracy required to perform automated landing which is a major goal for leading aviation companies like Airbus. The conventional INS/GPS solution has limitations due to weaknesses in GPS, exacerbated by challenging environmental conditions and increasing air traffic. The proposed methodology combines data from gyroscopes and accelerometers as inertial references, GPS as the primary observer, and Radio Altimeter (RA) and Instrument Landing System (ILS) as backup observer sensors when GPS is unreliable. An extended Kalman filter was developed and optimized using ground truth datasets to process the diverse sensor data. In addition, validation of the methodology was conducted using an X-plane plugin to simulate various landing scenarios at Montréal-Mirabel International Airport (CYMX) on runway 06. The results demonstrated improved positioning accuracy during the landing phase compared to the conventional INS/GPS solution, with a 50% enhancement in overall 3D positioning accuracy. The fusion approach offers several advantages over alternatives. It requires minimal hardware modifications to aircraft and airports, making it a cost-effective solution. Furthermore, it relies on radio avionic signals, reducing dependence on environmental conditions compared to vision-based solutions.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".