Passive tracking and detection of underwater narrow-band acoustical spectral signatures
Bibliographic record
Abstract
Tracking, detection and classification of targets is complicated in presence of multiple targets located at close or overlapping bearings. Assuming that the respective targets exhibit unique spectral signatures, which include narrow-band (NB) tonals and associated harmonics, this problem is addressed in several steps using conventionally beam-formed array data. The first two include detection of signals and associated bearings, followed by clustering bearings and associated frequencies. Signal and bearing detections are carried out independently analyzing their time-frequency distributions sorted along bearing in direction of descending spectral power. Values of power and bearings are grouped independently from each other into vectors, which in turn form sets called time-frequency (TF) cells. The Maximum Mean Discrepancy (MMD) test of empirical centres of masses of respective TF cells in an inner product space is a similarity measure used for required detections. Respective MMD's of power and bearing TF cells are used to obtain weights used to cluster detected bearings and associated frequencies. A signal detected at a given frequency is used to start a single frequency-bearing Kalman tracker (SFBT). A multivariate frequency-bearing tracker (MFBT) is started when the SFBTs associated by a common bearing are observed repeatedly at relative frequency exceeding a predefined threshold. A state vector of MFBT is propagated in time if detections are observed at least at two out of N associated SFBTs. For each SFBT frequency gates of few per cent of central frequency are used. Inconsistency check of latest MFTB bearings observed at different frequencies is used for MFTB stopping decision. Along bearing of each MFTB a mean spectrum is calculated. This spectrum corresponds to a target signature, which can be used for classification purposes in future.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".