Bootstrapping Particle Filters using Kernel Recursive Least Squares
Why this work is in the frame
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Bibliographic record
Abstract
Although particle filters are extremely effective algorithms for object tracking, one of their limitations is a reliance on an accurate model for the object dynamics and observation mechanism. The limitation is circumvented to some extent by the incorporation of parameterized models in the filter, with simultaneous on-line learning of model parameters, but frequently, identification of an appropriate parametric model is extremely difficult. This paper addresses this problem, describing an algorithm that combines kernel recursive least squares and particle filtering to learn a functional approximation for the measurement mechanism whilst generating state estimates. The paper focuses on the specific scenario when a training period exists during which supplementary measurements are available from a source that can be accurately modelled. Simulation results indicate that the proposed algorithm, which requires very little information about the true measurement mechanism, can approach the performance of a particle filter equipped with the correct observation model.
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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 | 0.000 |
| 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