Short range localization of Autonomous Underwater Vehicles
Bibliographic record
Abstract
The melting of Arctic ice and the resulting access provided to previously inaccessible regions of the Arctic Ocean has lead to various Arctic exploratory efforts by a number of nations. Canada is collecting Arctic bathymetry survey data in order to define the extent of its continental shelf in accordance with the United Nations Convention on the Law of the Sea. Supporting the collection of Arctic bathymetry data are two International Submarine Engineering, Explorer class, Autonomous Underwater Vehicles (AUVs). In order to reduce the risk inherent with under-ice AUV operations, Defence Research and Development Canada - Atlantic has designed and built a homing system and a localization system for each of the vehicles. The homing system enables the AUV to find its way to the source of an underwater acoustic signal at ranges in excess of 50 km. In this paper we shall present the localization system, which utilizes a field of acoustic modems allowing the AUV to determine its three dimensional position relative to a reference point. Enhancements to the localization method shall be discussed. These include improvements both at the surface station and the implementation of the algorithm in the vehicle. Lastly, short range localization results from the 2010 Arctic survey trial near Borden Island are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".