An Optimization-Based Parallel Particle Filter for Multitarget Tracking
Why this work is in the frame
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Bibliographic record
Abstract
Particle filters are used in state estimation applications because of their capability to solve nonlinear and non-Gaussian problems effectively. However, they have high computational requirements, especially in the case of multitarget tracking, where data association is the bottleneck. In order to perform data association and estimation together, an augmented state vector, whose dimensions depend on the number of targets, is typically used in particle filters. With data association, the computational load increases exponentially as the number of targets increases. In this case, parallelization is a possibility for achieving real-time feasibility in large-scale multitarget tracking applications. In the work presented here, an optimization-based scheduling algorithm, that is suitable for parallel implementation of particle filter, is presented. This proposed scheduling algorithm minimizes the total computation time for the bus-connected heterogeneous primary-secondary architecture. Further, this scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected ones. A new distributed resampling algorithm suitable for parallel computing is also proposed. Furthermore, a less communication-intensive parallel implementation of the particle filter without compromising tracking accuracy using an efficient load balancing technique, in which optimal particle migration among secondary processors is ensured, is presented. Simulation results demonstrate the tracking effectiveness of the new parallel particle filter and the speedup achieved using parallelization.
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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 | 0.000 | 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