A particle filter based on a constrained sampling method for state estimation
Bibliographic record
Abstract
Increasingly in practical applications, nonlinearity, non-Gaussianity, and constraint are considered when dealing with state estimation problems. This paper proposes a novel constrained particle filter (PF) approach for state estimation, where three constraint strategies are implemented: First, to ensure the validity of prior, prior particles are restrictedly sampled in the constraint region by a constrained inverse transform sampling method. Second, if constraints are imposed on the posterior, a constrained re-sampling method, similar to the existing acceptance/rejection constrained PF method, is proposed to restrict the posterior particles to be generated from the valid prior particles. Third, the validity of state estimation is ensured through adjustment of part of posterior particles according to the posterior density function of states, which is accomplished by deleting uniformly selected violated posterior particle and uniformly selected valid posterior particle for reproduction. Compared with the existing methods, the proposed method implements constraints with better physical interpretation, and involves no numerical optimization procedure and no restrictive assumptions about the distributions. Simulation results demonstrate its effectiveness.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".