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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".