Stability analysis of the discrete-time cubature Kalman filter
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Bibliographic record
Abstract
This study analyses the estimation error behaviour of the discrete-time cubature Kalman filter (CKF) for general nonlinear systems with nonlinear measurements. First, we show that, under certain conditions, estimation error of the CKF remains bounded. Further, we show that the careful selection of noise covariance matrices can enhance the filter stability against large estimation error. A modified-CKF is then proposed to improve the stability while reducing the steady state error. The proposed modified-CKF uses adaptive process and measurement noise covariances to cope with large estimation errors, and an ellipsoidal measurement validation gate to reject measurement outliers. Theoretical findings are verified by a series of numerical simulations. The simulation results signify that when the estimation error is large, traditional-CKF may lead instability while the modified-CKF rapidly converges to the true states. Further, the results show that the use of measurement validation gate can improve the robustness of the modified-CKF against measurement outliers.
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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) | 0.000 | 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