Doppler effects on acoustic ranging uncertainties on autonomous underwater vehicles in the Beaufort Sea
Bibliographic record
Abstract
Moving and depth-varying receivers, such as autonomous underwater vehicles (AUVs), provide a great tool for acoustic remote sensing applications. An array of acoustic sources can be used to provide long-range acoustic positioning for AUVs, but there are challenges in the form of subsea position uncertainties that can be exacerbated by Doppler delay shifts. During the Canada Basin Acoustic Propagation Experiment (CANAPE) two M1 Seagliders equipped with WHOI micromodem acoustic receivers were deployed during August 2017. Acting as moving receivers, the Seagliders navigated the Beaufort Sea in and around the CANAPE array, recording transmissions from the broadband acoustic sources at varying ranges and depths of 2–530 km and surface to 750 m, respectively. The sources transmitted 135-second linear frequency modulated signals with a bandwidth of 100 Hz centered around 250 Hz. This work focuses on the Doppler delay shift effects on acoustic ranging uncertainties using these signals. Using vehicle attitude measurements over the duration of the signal receptions, it was found that 91 percent of the acoustic receptions included ranging uncertainties of 10 m or more due to Doppler, with particular impact at closer ranges of 50 km or less.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".