Improved Kalman filtering through moment-based innovation gain strategies
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Bibliographic record
Abstract
This paper presents the moment-based Kalman filter (MKF), a novel sub-optimal estimation strategy designed to enhance robustness in systems subject to modeling uncertainties or external disturbances. Unlike the conventional Kalman filter, the MKF incorporates higher-order statistical moments of the innovation to inform its gain calculation, allowing for a more nuanced representation of the underlying noise and measurement error characteristics. The filter is structured as a predictor-corrector algorithm and maintains computational efficiency while offering improved adaptability in uncertain environments. A mathematical formulation of the MKF is provided, along with a proof of stability. Performance is evaluated using a simulated electrohydrostatic actuator (EHA) model undergoing a leakage fault. Results from the computational study demonstrate that the MKF provides more accurate state estimates than the standard Kalman filter, particularly under faulty or uncertain operating conditions.
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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 | 0.001 |
| Open science | 0.001 | 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 it