Exploiting environmental asymmetry for vessel localization from the vertical coherence of radiated noise
Bibliographic record
Abstract
A method to determine the range and bearing of a moving broadband acoustic source, such as a surface vessel, using the coherence measured on two omni-directional, vertically separated hydrophones is demonstrated using acoustic data recorded near Alvin Canyon on the New England shelf break. To estimate the vessel's range, two theoretical approaches, a half-space model and a Pekeris waveguide model based on normal modes, establish simple relationships between the broadband signal coherence and frequency, source range, and the vertical separation of the receiver hydrophones. A brute force inversion produces a passive acoustic estimate of vessel range. Rapidly changing bathymetry with large features, such as that near Alvin Canyon, produces azimuthal asymmetry in the plan-view coherence pattern about the receivers due to horizontal refraction, focussing, and the up- (down-) slope compression (extension) of modal interference patterns. For vessels with a constant speed and heading, this generates an asymmetry in the received power and vertical coherence fringing pattern. This effect is first demonstrated using reciprocal three-dimensional parabolic equation and raytracing models in an idealized Gaussian canyon, then observed in Alvin Canyon measurements. By comparing the experimental observations to the modeled coherence, the vessel's bearing and range relative to the receivers are obtained.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".