Results of the development of a long-range acoustic homing system for autonomous underwater vehicles
Bibliographic record
Abstract
Modified international submarine engineering (ISE) explorer AUVs with an endurance of over 400 km are being used to aid in the mapping of the under-ice Arctic seafloor. The explorers are equipped with a DRDC-developed acoustic homing system built into the AUV nose cone. The acoustic receiver consists of seven digital hydrophones arranged in a tri-axis cross-dipole array. A controller/data processor located within the AUV pressure hull handles the real-time acoustic arrival azimuth and elevation estimation, as well as the control and calculations for an on-demand short-range three dimensional (3-D) localization system, and the control of vehicle telemetry data flow. The processor and array consume less than 2 W. A small, easily transportable, DRDC-designed acoustic transducer provides a powerful acoustic homing signal. Using the homing system, the vehicles were able to locate an acoustic beacon at a randomly drifting Ice Camp from a range in excess of 50 km (100-km ranges possible). The design, development, and use of the homing system are described in this paper. In addition, on-going software improvements providing enhanced capabilities and system miniaturization are described.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".