A distributed consensus plus innovation particle filter for networks with communication constraints
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
Motivated by the problem of distributed signal processing in sensor networks, the paper considers the general problem of state estimation in geographically dispersed systems with nonlinear dynamics operating in an uncertain environment with communication constraints. Distributed particle filter implementations used as nonlinear state estimators introduce an additional consensus step, which must converge to achieve consistent values for local estimators' statistics in between two consecutive filter iterations. The number of consensus iterations per consensus run is high such that the consensus step may not converge in between two filter iterations especially in networks with intermittent connectivity. To reduce the consensus liability, we propose a consensus plus innovation based distributed implementation of the unscented particle filter (CI/DUPF), which extends the linear consensus and innovation framework to nonlinear distributed estimation. The CI/DUPF does not require the consensus step to converge and is suited for environments with intermittent connectivity. In our Monte Carlo simulations, the performance of the CI/DUPF follows that of its centralized counterpart even with a limited number of consensus iterations per consensus run.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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