Smooth Variable Structure Filter in Random Finite Set Applications
Bibliographic record
Abstract
Data association and filter robustness are two key problems for general multiple target tracking. Classic data association methods have high computational complexity, and the gold standard Kalman Filter (KF) suffers when the motion model does not match target behavior. Random Finite Set (RFS) based filters such as the Probability Hypothesis Density (PHD) filter solves the computational complexity issue of data association by using one multiple-target filter instead of multiple single-target filters in parallel, and the Smooth Variable Structure Filter (SVSF) improves filter robustness by bounding the estimation error using sliding mode control theory. However, there has not been an attempt to merge these two filtering strategies in literature. This paper presents 3 contributions to this problem: a methodology to combine the SVSF filtering strategy into the PHD filtering framework, results that show improvement in performance over traditional PHD filtering, and modifications to improve computational complexity. Three nonlinear filters are created and tested against the two common nonlinear PHD filters in a complex simulation with time varying targets, model mismatch, and high process noise. The variants show stability and robustness in situations where the original filters diverges. The filters are evaluated using the Optimal Subpattern Assignment (OSPA) metric.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".