The Auxiliary Extended and Auxiliary Unscented Kalman Particle Filters
Bibliographic record
Abstract
This paper proposes two new particle filters, namely, the auxiliary extended Kalman particle filter (AEKPF) and the auxiliary unscented Kalman particle filter (AUKPF). The theory governing the newly proposed filtering techniques is developed and the algorithms are described and contrasted. Next, a series of tests is presented in which the new filters are compared against the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and several existing particle filters. The test results are from simulations with synthetic mathematical models that incorporate elements that are nonlinear, non-stationary, and stochastic. Performance results are presented for various degrees of model nonlinearity including first, second, and third order systems. Furthermore, experimental results are also reported comparing the filters performances with different signal to noise ratios and noise models, including Gaussian, Cauchy, and Gamma distributions. Various metrics are used to compare the filters performances and to make conclusions about future work. It is shown to be advantageous to use certain particle filters depending on the noise distribution of the system of interest. In particular, the AUKPF and the AEKPF outperform existing particle filters in many cases.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".