Quadratic-Kalman-Filter-Based Sensor Fault Detection Approach for Unmanned Aerial Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
Sensors are crucial for the control of unmanned aerial vehicles (UAVs). However, sensor faults will inevitably appear over time. Therefore, it is important to develop a sensor fault detection approach for the reliability of UAV. This article presents a novel model-based UAV fault detection approach based on quadratic Kalman filter (QKF). First, an accurate kinematic and dynamic model of UAVs is established, where the model is linearized and discretized for Kalman filter (KF). Second, the first KF is used for denoising, the secondKF is used to detrend, and residuals are calculated for detection. It is worth mentioning that the second KF is a modified Sage–Husa adaptive KF, which can automatically estimate the measurement noise variance. Compared with traditional approaches, this approach has the advantages of noise reduction, self-adaptation, divergence avoidance, and high detection rate. Simulation and experimental results show the effectiveness of the proposed approach, which can accurately detect the abrupt and incipient fault of an inertial measurement unit (IMU) sensor. At the same time, it can get the higher fault detection rates (FDRs) compared with conventional KF. Furthermore, this approach also provides the leading information and foundation for UAV fault-tolerant control.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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