Attitude and heading reference system for small unmanned aircraft collision avoidance maneuvers
Bibliographic record
Abstract
This thesis describes the development of an Attitude and Heading Reference System (AHRS) to sense three-dimensional orientation for collision avoidance control in small unmanned aircraft. Unmanned aircraft are currently restricted to flight in designated airspace due to safety concerns of collision with manned aircraft. Therefore, collision avoidance is necessary to ensure the safety of both aircraft. Technical challenges, mainly in sensor limitations, restrict AHRS performance in attitude estimation during high-g maneuvers. Using sensor filtering techniques and a robust attitude representation, an AHRS suitable for collision avoidance is developed. Acceleration disturbances are reduced using estimates of non-gravitational accelerations including centripetal acceleration and model-based acceleration to improve gravity vector measurement during aircraft maneuvers. Simulation results with a variety of maneuvers deemed challenging for most AHRS are given showing accurate attitude estimates. Flight data from an existing commercial autopilot is compared with the results of the AHRS to demonstrate the validity of the solution with real flight data.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".