Wasserstein distance for the fusion of multisensor multitarget particle filter clouds
Bibliographic record
Abstract
In a multisensor multitarget tracking application, the evaluation of the cost of assigning particle filter clouds of different sensors as being estimates of the same target is an essential part in the particle cloud association. This paper treats the problem of evaluating the cost of particle filter clouds association based on the Wasserstein distance of different orders, analyzing the implications of clouds cardinality (for weighted particles), and of various resampling methods (for unweighted particles). As the Wasserstein distance at cloud level needs to have defined internally a metric at the particle level, the implications of using therein the Euclidean (for position components only) or Mahalanobis (including higher order components) distances are investigated. The crosscovariance of particle filter clouds is also estimated using the same Wasserstein distance and its introduction in the metric therein is explored. As a conclusion of various simulations, the design of the Wasserstein distance that is found to fit best the purpose of cloud-to-cloud association is presented.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".