Surface-constrained continuous-time extended Kalman filter: optimal estimation of a state of a constrained dynamic system-ball-rover rolling on a known surface
Bibliographic record
Abstract
The Surface-constrained Continuous-time Extended Kalman Filter (SCEKF), derived in thesis, contains a novel approach for handling surface or equality constraints, in which the surface-constrained CEKF is the projection of the unconstrained CEKF onto the set of state estimate rates that satisfy the constraints. The filter is used for optimal estimation of a state of a ball rolling on a known surface with uneven elevation. The state consists of surface contact point and geometrical center positions, attitude and angular velocity of the ball. The dynamics of the ball is affected by "unknown" to the filter disturbances, due to off-center point mass and variable wind. Thesis includes derivations of the SCEKF and the constraint dynamics of a rolling ball. The numerical computation results show that the surface-constrained filter can produce an accurate state estimate of the rolling ball and demonstrate that the estimate is significantly better than that produced by unconstrained filter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".