<title>Track-to-track association using informative prior associations</title>
Bibliographic record
Abstract
In a single-frame track-to-track association, due to the local sensors track swapping (switching of the track from an estimated target to another estimated target, under measurement uncertainty conditions), the identities of the fused tracks over several frames are not preserved. The main goal of the proposed track-to-track association method is to link the histories of fused tracks over several frames and avoid track swapping at the fusion center level (e.g. to preserve the continuity of the fused tracks through their identities). In this method, the previous association hypotheses are taken as priors in a multiple-hypothesis association chain. The continuity of the fused tracks over several frames is achieved through the prediction of the fused tracks obtained from a set of best association hypotheses at each frame. Through this, if in computing the fused tracks estimation errors, their identities are taken into account (e.g. the errors of a fused track over all the frames are computed with respect to the same true target), this procedure will improve also the fused track state estimation error. The method and implementation proposed is intended to be used to identify the histories of two or more tracks at the fusion center, and possibly to improve the track-to-track association.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 0.070 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".