A neural network-based detection thresholding scheme for active sonar signal tracking
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Bibliographic record
Abstract
Intensity thresholding is an effective technique to cut off the low energy noises and cut down the computational load in an underwater target tracking system. A neural network based adaptive intensity thresholding scheme with a constant false alarm rate (CFAR) for an active sonar signal tracking situation in a realistic sea environment is proposed in this paper. The proposed system has the following advantages: (1) It performs well in a nonhomogenous sea environment; the false alarm rate is kept constant while the threshold changes with different sea environments; (2) It can adaptively estimate the threshold for different range cells because the noise under estimation is strictly local so that the received intensities of noise and targets are not affected by the distance they travel; and (3) The computational requirements are moderate.
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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 | 0.000 | 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