Sharpening Sidescan Sonar Images for Shallow-Water Target and Habitat Classification With a Vertically Stacked Array
Bibliographic record
Abstract
Sidescan imagery is commonly used to provide a 2-D real-time “look” at the seafloor in high resolution. In shallow-water environments, however, interference from surface scattering and from multipath signals may deteriorate the quality of the sidescan images to the point where target detection and classification are no longer achievable. This paper investigates the extent to which this interference may be suppressed by a small array which employs across-track beamforming. A sidescan array utilizing a vertical stack of six receive elements was constructed, and is shown to be effective at providing a clear view of the seafloor when surface and multipath interference is present. A theoretical analysis examines the relative path strengths of the received signals for different across-track beam patterns, and examines how these signals are affected when beamforming is applied on transmit and/or receive. Shadow contrast reduction caused by the along-track beamwidth is also considered for different along-track beam patterns and different target widths. A simulator was created to illustrate the impact of interference contributing to the received signal, and to show how the received signal is affected when beamforming is applied. The across-track beam patterns of the experimental array were measured, and these data are used to enhance the theoretical and simulated predictions of received signal strength. Experimental data are also presented in which a sidescan image, heavily contaminated by interference, is significantly improved using across-track beamforming. It is concluded that a vertically stacked multielement sidescan sonar which employs across-track beamforming on receive is a valuable tool for suppressing the multipath and surface interference which arise in shallow-water surveys.
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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) | 0.001 | 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) | 0.001 | 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".