<title>Tracking spawning targets with a tagged particle filter</title>
Bibliographic record
Abstract
The particle filter is an effective technique for target tracking in the presence of nonlinear system model, nonlinear measurement model or non-Gaussian noise in the system and/or measurement processes. However, the current particle filtering algorithms for multitarget tracking suffer from high computational requirements. In this paper, we present a new implementation of the particle filter, called the tagged particle filtering (TPF) algorithm, to handle multitarget tracking problems in an efficient manner. The TPF uses a separate sets of particles for each track. Here, each particle is associated with the closest (in terms of likelihoods) measurement. The particles for a particular track may form separate groups in terms of the measurements associated with them and they evolve independently in groups till two or more groups of particles are separated by a distance large enough to be called separate tracks. A decision is made as to which of the groups is to be retained. Since this algorithm keeps a separate set of particles for each track, the state estimation for individual tracks does not require any additional computation. Also, this algorithm is association free and target class information can be added to the state for feature aided tracking. Simulation results are obtained by applying this tracking filter to a spawning target scenario.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.058 | 0.045 |
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".