Sensor Fault Diagnosis for Unmanned Quadrotor Helicopter via Adaptive Two-Stage Extended Kalman Filter
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a new method to address the problem of sensor Fault Detection and Diagnosis (FDD) of an unmanned quadrotor helicopter. Firstly, in order to eliminate the effect of model uncertainties the gyroscopic effects, the kinematic model of the unmanned quadrotor helicopter replacing the traditional dynamic equations is simply presented. Then, through introducing a set of forgetting factors into the general two-stage extended Kalman filter which is capable of estimating the system states and the faults simultaneously, an Adaptive Two-Stage Extended Kalman Filter (ATSEKF) is developed to diagnose the sensor faults more quickly and accurately. Finally, the proposed approach is validated in three different scenarios including without fault, faults occurred separately and simultaneously. The simulation results demonstrate the effectiveness of the proposed FDD method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it