Full order distributed particle filters for intermittent connections: Feedback from fusion filters to local filters improves performance
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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