Improving direction-sensing by multibeam sonar
Bibliographic record
Abstract
The complexity of multibeam sonar systems makes their beamforming susceptible to amplitude and phase distortion, e.g., due to environmental changes. A form of calibration is usually performed in situ over a flat area to ensure flatness in the resulting sonar image. However, lack of detailed knowledge of individual channel performance prevents application of an amplitude-weighting function such as the Blackman or Hamming type, which could otherwise be used to reduce the impact of sidelobes without damaging signal quality. Two radically different solutions are proposed: application of the Vernier principle [G. Llort-Pujol, Oceans 2006 MTS/IEEE Conf. Proc. (Quebec City, Canada, 2008); C. Sintes et al., Oceans 2011 MTS/IEEE Conf. Proc. (Waikoloa, HI, 2011)] to interferometry performed at low grazing angles, and performance of a standard-target calibration [K. G. Foote et al., J. Acoust. Soc. Am. 117, 2013 (2005)] to measure the two-way sensitivity of individual channels directly. The two methods, which are also applicable to sidescan sonar, are elaborated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".