Incorporating real-time acoustic ranging and glider-based Doppler measurements to aid vehicle navigation
Bibliographic record
Abstract
In the summer of 2017, two Seagliders equipped with low frequency acoustic recorders and 1 MHz acoustic Doppler current profilers (ADCPs) were deployed in the Canada Basin as part of a large-scale acoustic tomography experiment. Acoustic tomography sources were moored at approximately 175 m depth within an acoustic duct enabling acoustic transmission to be received at long ranges. The sources transmitted at a center frequency of approximately 250 Hz every four hours, and ranges between the sources and Seaglider were estimated in real time using a WHOI MicroModem. These ranges can be incorporated into an extended Kalman filter for navigation. In addition, glider-mounted upward-looking ADCPs recorded ~26.5 m shear profiles every 15 seconds. These overlapping profiles can be used to estimate (in post-processing) both the local current profile on a per-dive basis and the glider's relative velocity through the water. In real time, the close-range ADCP velocity measurements can be used to estimate the glider's relative (Through The Water, TTW) velocity to improve the glider's subsea position estimate. We will describe our recent developments in using these Doppler measurements to aid glider navigation, comparing the results to previous developments in range-aided navigation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".