Multi-target tracking for measurement models with additive contributions
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
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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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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