Full order distributed particle filters for intermittent connections: Feedback from fusion filters to local filters improves performance
Bibliographic record
Abstract
In [1, 2], we proposed a consensus/fusion based distributed implementation of the particle filter (CF/DPF) for non-linear systems with non-Gaussian excitation and intermittent communication connectivity. To recap, the CF/DPF implemented two filters at each node: (i) A localized particle filter based only on the host node's observations, and; (ii) A separate consensus-based filter (fusion filter) to fuse together the local filters' densities for estimating the global posterior in a distributed fashion. At each sensor node, the fusion filter provides the overall state estimates. The paper extends the CF/DPF framework by incorporating feedback from the fusion filter back to the local particle filter - a proposed enhancement to the original CF/DPF. No additional communication overhead is needed for the feedback modified CF/DPF (FCF/DPF), which exhibits improved overall performance over the CF/DPF in highly noisy Monte-Carlo simulations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".