Constrained Abridged Gaussian Sum Extended Kalman Filter: Constrained Nonlinear Systems with Non-Gaussian Noises and Uncertainties
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Bibliographic record
Abstract
This work presents a constrained abridged Gaussian sum extended Kalman filter (constrained AGS–EKF) that employs Gaussian mixture models to improve the estimation of extended Kalman filter (EKF) for constrained nonlinear applications involving non-zero mean non-Gaussian process uncertainties and measurement noises. The posterior estimation step in EKF is modified to adopt non-Gaussian measurement noises. An intermediate step is considered to approximate the non-Gaussian prior distribution of the constrained states at each sampling interval. This modified EKF also considers the modified prior estimation step proposed in AGS–EKF (to capture the non-Gaussian process uncertainties). Constrained AGS–EKF performs one (modified) EKF based on the mean value and covariance matrix of the overall Gaussian mixture model, thus avoiding additional computational costs and biased estimations observed in conventional Gaussian sum filters. Computational experiments were performed and showed that the proposed constrained AGS–EKF scheme is computationally efficient and provides appropriate estimates for applications involving active constraints on states, non-Gaussian process uncertainties, and measurement noises.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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