Constrained Extended Kalman Filter based on Kullback-Leibler (KL) Divergence
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Bibliographic record
Abstract
Extended Kalman Filter (EKF) is one of the most extensively used state estimator for nonlinear systems. As this technique cannot handle constraints, it might result in physically meaningless state estimates. Therefore, in this work, we focus on imposing inequality constraints in the state estimation problem to obtain physically meaningful state estimates as well as improve the estimation accuracy. For this purpose, we project the unconstrained EKF solution into the constrained region by minimizing the Kullback-Leibler (KL) divergence. The proposed constrained EKF framework updates the values of the states and error covariances by solving the convex optimization problem involving conic constraints. The efficacies of the proposed algorithm are demonstrated in a batch reaction system, and the performance of the proposed approach is found to outperform the recursive nonlinear dynamic data reconciliation solution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it