An Adaptive GLMB Filter with Unknown Environmental Parameters
Why this work is in the frame
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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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it