Distributed Soft-Data-Constrained Multi-Model Particle Filter
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".