Unconstrained underwater multi-target tracking in passive sonar systems using two-stage PF-based technique
Why this work is in the frame
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Bibliographic record
Abstract
A robust particle filter (PF)-based multi-target tracking solution for passive sonar systems able to track an unknown time-varying number of multiple targets, while keeping continuous tracks of such targets, is presented in this article. PF is a nonlinear filtering technique that can accommodate arbitrary sensor characteristics, motion dynamics and noise distributions. An enhanced version of PF is employed and is called Mixture PF. The commonly used sampling/importance resampling PF samples from the prior importance density, while Mixture PF samples from both the prior and the observation likelihood. In order to be able to track an unknown time-varying number of multiple targets, two Mixture PFs are used, one for target detection and the other for tracking multiple targets, and a density-based clustering technique is used after the first filter. This article demonstrates the applicability of the proposed technique for the passive problem, which suffers from the lack of measurements and the small detection range of the buoys, especially for weak signals. A contact-level simulation was used to generate different scenarios and the performance of the proposed technique called Clustered-Mixture PF was examined with either bearing measurement only or bearing and Doppler measurements, and it demonstrated its high performance.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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