Classifying Continuous Active Sonar Echoes for Target Recognition
Bibliographic record
Abstract
Abstract : Classification and tracking are two important techniques for enhancing active sonar performance. Classification rejects unwanted clutter using echo analysis, and tracking provides a history of target motion while rejecting clutter that doesnt support realistic target motion. Continuous active sonar (CAS) has been proposed as an alternative to conventional pulsed active sonar (PAS), largely in order to provide tracking updates at a much higher rate than is possible with PAS. Unfortunately, these faster updates come at the cost of reduced classification performance, at least for CAS that uses linear frequency modulated waveforms. In this case, maximizing the update rate requires sub-band processing. Classification of echoes from these sub-bands is expected to be relatively poor, since the full bandwidth is favoured for classification. An alternate processing scheme for CAS uses full-band processing, which is typically used for PAS. This potentially maximizes classification performance rather than providing faster updates as in the sub-band approach. A risk of this scheme is the potential for complications in echo signals arising from coherence loss caused by the long duration of CAS waveforms. One facet of a recent Canada-U.S. sea trial, TREX13, focused on conducting experiments that allow direct comparison of the performance of CAS and PAS in shallow water. In this paper, DRDCs echo classification software was tested with sonar echoes from TREX13. The software, which was originally developed for PAS applications, was used to evaluate whether CAS echoes can be classified as accurately as PAS echoes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".