Enhanced sequential nonlinear tracking filter with denoised pseudo measurements
Why this work is in the frame
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Bibliographic record
Abstract
Sequential nonlinear tracking filter using pseudo measurements has been proposed to solve the tracking problem with range-rate measurements. Replacing the range-rate measurement by pseudo measurement constructed by the product of range and range-rate measurements can reduce nonlinearity, but large covariance of the error of pseudo measurements may be introduced. A denoising method based on a debiased Kalman filter is proposed in this paper to reduce the error of pseudo measurements. Then the denoised pseudo measurements are processed sequentially with position measurements to establish a new tracking filter with range-rate measurements. The proposed filtering method can reduce not only the nonlinearity but also the error of pseudo measurements. Monte Carlo simulations show that the performance of the new tracking filter is better than the sequential filter using pseudo measurement without denoising.
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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 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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