Online clutter estimation using a Gaussian kernel density estimator for multitarget tracking
Bibliographic record
Abstract
In this study, the spatial distribution of false alarms is assumed to be a non‐homogeneous Poisson point (NHPP) process. Then, a new method is developed under the kernel density estimation (KDE) framework to estimate the spatial intensity of false alarms for the multitarget tracking problem. In the proposed method, the false alarm spatial intensity estimation problem is decomposed into two subproblems: (i) estimating the number of false alarms in one scan and (ii) estimating the variation of the intensity function value in the measurement space. Under the NHPP assumption, the only parameter that needs to be estimated for the first subproblem is the mean of false alarm number, and the empirical mean is used here as the maximum likelihood estimate of that parameter. Then, for the second subproblem, an online multivariate local adaptive Gaussian kernel density estimator is proposed. Furthermore, the proposed estimation method is seamlessly integrated with widely used multitarget trackers, like the joint integrated probabilistic data association algorithm and the multiple hypotheses tracking algorithm. Simulation results show that the proposed KDE‐based method can provide a better estimate of the false alarm spatial intensity and help the multitarget trackers yield superior performance in scenarios with spatially non‐homogeneous false alarms.
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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 | 0.006 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".