Simultaneous State and Parameter Estimation with Trajectory Shape Constraints (Poster)
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
In some tracking scenarios, the target state is subjected to equality constraints due to external limitations or inherent properties. If the constraints are known a priori, more accurate state estimates can be produced by taking advantage of these additional information in tracking algorithms. In this paper, a new model of the trajectory shape constraint is proposed when the target trajectory is known to be a straightline. The unknown slope and intercept of the straightline are treated as states to be estimated along with the target state. Then, two pseudo-measurements are constructed and augmented into the measurement equation in the filtering process. A trajectory shape constraint augmented state filter (TSC-ASF) is developed to produce constrained state estimates and constraint parameter estimates simultaneously. The nonlinear radar measurements and pseudo-measurements are processed by the converted measurement Kalman filter (CMKF) and unscented Kalman filter (UKF), sequentially. The unscented transform (UT) is employed to initialize the filter. Monte-Carlo simulation results illustrate the effectiveness of the proposed algorithm.
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 | 0.000 |
| 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