Distributed Particle Filter Implementation with Intermittent/Irregular\n Consensus Convergence
Why this work is in the frame
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Bibliographic record
Abstract
Motivated by non-linear, non-Gaussian, distributed multi-sensor/agent\nnavigation and tracking applications, we propose a multi-rate consensus/fusion\nbased framework for distributed implementation of the particle filter (CF/DPF).\nThe CF/DPF framework is based on running localized particle filters to estimate\nthe overall state vector at each observation node. Separate fusion filters are\ndesigned to consistently assimilate the local filtering distributions into the\nglobal posterior by compensating for the common past information between\nneighbouring nodes. The CF/DPF offers two distinct advantages over its\ncounterparts. First, the CF/DPF framework is suitable for scenarios where\nnetwork connectivity is intermittent and consensus can not be reached between\ntwo consecutive observations. Second, the CF/DPF is not limited to the Gaussian\napproximation for the global posterior density. A third contribution of the\npaper is the derivation of the exact expression for computing the posterior\nCramer-Rao lower bound (PCRLB) for the distributed architecture based on a\nrecursive procedure involving the local Fisher information matrices (FIM) of\nthe distributed estimators. The performance of the CF/DPF algorithm closely\nfollows the centralized particle filter approaching the PCRLB at the signal to\nnoise ratios that we tested.\n
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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