A Transfer State Estimator for Uncertain Parameters and Noise Statistics
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel approach to tackle uncertainties in model parameters and noise statistics for state estimation. The proposed method leverages transfer learning to combine the strengths of the unbiased finite impulse response (UFIR) filter and the Kalman filter (KF), with UFIR serving as the source domain filter and KF as the target domain filter. To bolster the robustness of state estimation within the target domain, the proposed method transfers the predicted state probability density functions (pdfs) from UFIR and fine-tunes the error covariance of the KF filter to achieve seamless integration. Unlike conventional fusion techniques, this method avoids the need for UFIR’s error covariance, thus mitigating its adverse impact on estimation accuracy. We demonstrate the competitiveness of this transfer state estimator in handling parameter uncertainties through moving target tracking, showing superior performance compared to existing fusion methods for state estimation.
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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 | 0.000 | 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