Selectively iterative particle filtering and its applications for target tracking in WSNs
Bibliographic record
Abstract
Particle filters (PF) have been widely used in the estimation of the state transition and observation of non-linear/non-Gaussian systems, and samples degeneracy is the main issue of particle filters. In this paper, a novel PF - selectively iterative particle filter (SIPF) is proposed for target tracking in wireless sensor networks (WSNs). There are two novel strategies in SIPF, the statistics based threshold and particles refining. The key insight of SIPF is from an experimental observation that, the more suitable divergence of particles can yield the better estimation. The performance of the proposed SIPF is tested on two theoretical models, and then it is used in a target tracking issue in WSN in the distributed model. Experimental results show that the proposed SIPF can greatly improve the accuracy of object tracking, and in theoretical models the estimation error is only about 10%, while in practical models is only about 25% compared to other existing 9 filtering methods.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".