Extended and unscented Kalman filters for the identification of uncertainties in a process
Bibliographic record
Abstract
This paper describes the application of extended and unscented Kalman filters for the identification of uncertainties in a process. The extended Kalman filter (EKF) is an optimal linear recursive algorithm that offers a solution to the filtering problem. The EKF is based on a first-order Taylor expansion to approximate the measurement and process models. This approach may cause the estimation process to diverge. Consequently, alternatives (e.g., the unscented Kalman filter, UKF) based on a fixed number of points to represent a Gaussian distribution have been introduced. The EKF and UKF have been applied for the identification of uncertainty in the attitude determination process for small satellites based on noisy measurements collected from Sun sensors and three-axis magnetometers. Simulation results indicate that the EKF and UKF perform equally well when small initial errors are present. However, when large errors are introduced, the UKF leads to a faster convergence and achieves a higher more accurate estimate of the state of the system.
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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) | 0.000 | 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 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".