Target tracking using particle filter with X-band nautical radar
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a particle filter (PF) method is proposed for target tracking based on a practical X-band nautical radar image sequence, using two histogram-based target models: a kernel-weighted histogram model and a background-weighted histogram model. In particular, the sampling importance resampling (SIR) particle filter is implemented to provide a recursively probabilistic estimation procedure for target tracking. For the measurement model of the particle filter, a Bhattacharyya coefficient based similarity function is utilized to compare the reference target and target candidate, which are represented by the histogram of the target area in the radar image. Within the PF tracking procedure, both the kernel-weighted and background-weighted models are applied with estimated target tracks compared with the practical GPS data. The experiment shows that both models can effectively complete the target tracking task with the kernel-weighted model performing better when the reference model is accurately selected, with the background-weighted model being more flexible.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it