Toward Enhancing Quadrotor Flight Accuracy: Extended and Unscented Kalman Filter Estimation in SE(3)
Bibliographic record
Abstract
This study evaluates the performance of the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) in estimating the states of a quadrotor modeled in SE(3). Estimation is crucial for autonomous flights and research-based quadrotors, where accuracy and stability are essential. The EKF employs a linearized estimation approach, whereas the UKF uses a nonlinear estimation approach. The quadrotor, modeled in SE(3), is controlled using a geometric controller. A total of 18 states are estimated using measurements from the Inertial Measurement Unit (IMU) and the Global Positioning System (GPS). The performance of the two estimators was assessed through numerical simulations. Both the EKF and the UKF demonstrated similar performance; however, the UKF performed slightly better than the EKF. This finding suggests that, under highly nonlinear conditions, the UKF is the preferable choice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".