An Adaptive GLMB Filter with Unknown Environmental Parameters
Bibliographic record
Abstract
In the context of multi-target tracking based on random finite sets, the main challenge lies in the random fluctuations in both the number and state of targets. In most target tracking application scenarios, model parameters such as clutter rate and detection probability are unknown and time-varying; in addition, the birth intensity of nascent targets is difficult to predict. All of this information significantly affects the tracking accuracy of the filter and is usually assumed to be known and provided by the user. In this study, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter capable of estimating the unknown parameters and the birth intensity of nascent targets. This filter combines the measurement-driven method and uses the robust multi-Bernoulli filter to calculate the clutter rate and detection probability, which can track multiple targets without the need for priori information. The simulation results demonstrate that the proposed method significantly improves the multi-target tracking performance, outperforming the CBMeMBer and CPHD filters with known tracking scene information in terms of tracking effectiveness. Meanwhile, the proposed method achieves tracking performance that is comparable to that of the GLMB filter.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".