Multi-target tracking for measurement models with additive contributions
Bibliographic record
Abstract
Abstract—Moment-based filters, such as the Probability Hy-pothesis Density (PHD) filter, are an attractive solution to multi-target tracking. However, an underlying assumption for the PHD filter is that each measurement is either caused by a single target or clutter. In this paper, we design a novel moment-based multi-target filter, the Additive Likelihood Moment (ALM) filter, where the measurements are affected by all targets. We focus on the cases where the likelihood can be expressed as a function of the sum of the individual target contributions. As an example, we consider radio tomographic tracking where the attenuation of the signal between a pair of sensors is the sum of attenuations caused by all targets. Our multi-target tracking algorithm is based on a particle approximation of our moment-based filter. Our simulations show that our algorithm has a lower estimation error than MCMC particle methods while achieving 80 % savings in terms of computational time.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".