Striation Processing of Data From the 2013 Target and Reverberation Experiment (TREX13)
Bibliographic record
Abstract
In active sonar processing, the discrimination of the target versus clutter can be a significant challenge. Whereas conventional processing often uses target kinematics to limit the possible target tracks, it has recently been shown that frequency domain information (based on the waveguide invariant principle) can also be incorporated to further limit the possible target tracks in an environmentally robust fashion. This paper presents physics-based signal processing methods to extract information about the target track from striations in a target spectrogram formed from the echo spectra at each active sonar pulse repetition interval. The target tracking information is formulated as a post-track likeliness statistic that is extracted from the sonar data with image processing techniques. Results are demonstrated with shallow water sonar data collected during the 2013 Target and Reverberation Experiment (TREX13). It is expected that the physics-based signal processing algorithms discussed here will provide enhanced clutter rejection and improve tracking performance.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".