Consensus-Based Labeled Multi-Bernoulli Filter With Event-Triggered Communication
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multi-target tracking (MTT) in a communication resource-sensitive distributed sensor network (DSN). Although consensus-based approaches provide effective tools for distributed fusion and MTT, the requirement of iterative communication makes it impractical in resource limited situations. To deal with this issue, two event-triggered strategies are proposed and incorporated into the consensus-based LMB. Focusing on the information discrepancy between the local multi-target probability density function (PDF) and the time prediction of the latest broadcast one, the integral-triggering strategy (ITS) is introduced. Furthermore, by proving that the information discrepancy (Kullback-Leibler divergence) between two LMB densities with the same label space can be decomposed into the sum of the information discrepancy of each LMB component pair (LMB components with the same label), the separated-triggering strategy (STS) is proposed. The performance of the proposed algorithms is demonstrated in a distributed multi-target tracking scenario via numerical simulations.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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