Probabilistic data association in high clutter environments
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
Data association is the key component in single or multiple target tracking algorithms with measurement origin. Probabilistic Data Association (PDA), in which all validated measurements are associated probabilistically to the predicted estimate, is a well-known method to handle the measurement origin uncertainty. In PDA, the effect of measurement origin uncertainty is incorporated into the updated covariance by adding the spread of the innovations term. The updated covariance may become very large after few time steps in high clutter scenarios due to spread of the innovations term. Large covariance results in a large gate, which is used to limit the possible measurements that could have originated from the target. Hence, the track will be lost and estimate will just follow the prediction. Also, large gate will make the well-separated target assumption invalid, even if the targets are well-separated. Hence, after a few time steps all the targets in the surveillance region come under the same group, making the Joint Probabilistic Data Association (JPDA). In this paper, adaptive gating techniques are proposed to avoid the steady increase in the updated covariance in high clutter. The effectiveness of the proposed techniques is demonstrated on simulated data.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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