Wasserstein distance for the fusion of multisensor multitarget particle filter clouds
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it