Innovative Formulation in Discrete Kalman Filtering with Constraints - A Generic Framework for Comprehensive Error Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This manuscript establishes a generic framework for comprehensive error analysis in discrete Kalman filtering with constraints, which systematically provides a complete set of algorithmic formulas along with demonstrating an alternative process of theoretical analytics of discrete Kalman filter. This constructive work aims extensively to standardize the formulation of Kalman filter with constraints. In analogy to the similar framework for standard discrete Kalman filter (without any constraints), the proposed framework specifically considers: model formulation vs. the error sources, the solution of the state and process noise vectors, the residuals for the process noise vector and the measurement noise vector, the redundancy contribution of the predicted state vector, process noise vector and measurement vector, and other relevant essential aspects, of which some of the features are essential to comprehensive error analysis, but are nonexistent yet in the primary algorithm in Kalman filtering with constraints. Besides, the algorithmic form of the Extended Kalman filter with constraints is also provided for practical purpose. At the end, specific remarks about the developed framework are given to emphasize on its usage to a certain extend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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