Time-Varying Across-Track Beamforming for the Suppression of Bottom-Bounce Multipath Effects in Sidescan Sonar
Bibliographic record
Abstract
Sidescan sonars are used to provide a high-resolution 2-D image of the seafloor, but when used in shallow water, these side-looking systems are vulnerable to multipath interference. In some cases, this interference affects image interpretation and downstream processing such as target recognition or bottom classification. However, it is possible to suppress multipath interference by using a small array featuring a vertical stack of receivers. Multipath signals that arrive from the direction of the surface are easily suppressed using across-track receive beamforming, however multipath signals that arrive from the seafloor are not so easily removed. This paper investigates the use of time-varying across-track receive beamforming as a method to suppress these bottom-bounce signals. Two sidescan images are presented that illustrate the impact that bottom-bounce multipath can have on sidescan sonar images. A theoretical model is presented that gives the relative intensity of the received signals and illustrates how their intensities are changed by altering the receive beampattern. In the first example, a bottom-surface-bottom signal arriving from nadir is suppressed by simply reducing the extent of the main lobe before the signal is received. In the second example, two multipath signals arriving near broadside are suppressed by introducing a null into the main lobe. It is concluded that an array employing the proposed beam processing is capable of rejecting bottom-bounce multipath, assuming that the angle and time of arrival of the interference and bottom signal are known.
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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 | 0.000 |
| 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".