Distributed Soft-Data-Constrained Multi-Model Particle Filter
Why this work is in the frame
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Bibliographic record
Abstract
A distributed nonlinear estimation method based on soft-data-constrained multimodel particle filtering and applicable to a number of distributed state estimation problems is proposed. This method needs only local data exchange among neighboring sensor nodes and thus provides enhanced reliability, scalability, and ease of deployment. To make the multimodel particle filtering work in a distributed manner, a Gaussian approximation of the particle cloud obtained at each sensor node and a consensus propagation-based distributed data aggregation scheme are used to dynamically reweight the particles' weights. The proposed method can recover from failure situations and is robust to noise, since it keeps the same population of particles and uses the aggregated global Gaussian to infer constraints. The constraints are enforced by adjusting particles' weights and assigning a higher mass to those closer to the global estimate represented by the nodes in the entire sensor network after each communication step. Each sensor node experiences gradual change; i.e., if a noise occurs in the system, the node, its neighbors, and consequently the overall network are less affected than with other approaches, and thus recover faster. The efficiency of the proposed method is verified through extensive simulations for a target tracking system which can process both soft and hard data in sensor networks.
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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 | 0.000 |
| 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