The autonomous underwater vehicle emergency localization system : an under ice AUV tracking technology for over-the-horizon operations
Bibliographic record
Abstract
There is an inherent risk of loss that accompanies any operations of Autonomous Underwater Vehicle (AUV) technology. This complexity and risk are increased for AUV missions that are conducted beneath ice and in harsh environmental conditions (i.e. extreme cold, compromised visibility, etc.). Risk-based methodologies have been developed to quantify the risk of loss for specific AUV platforms prior to deployments. Their goal is to identify and mitigate where possible the significant contributors (technical or otherwise) to the overall risk of a specific operation. Not surprisingly, there is an abundant amount of literature related to successful AUV missions; however, there has been very little published work related to AUV loss. Specifically, this author is not aware of any examples of a developed procedure to employ during an AUV loss event to date, much less specific algorithms developed to locate a missing AUV. This is a subset of the AUV tracking or positioning that is rarely given specific treatment. The motivating problem is based on the loss event of an AUV during polar operations. For example, (i) the vehicle might navigate outside of its predefined spatial area through some fault or error, or, (ii) its mission involves over-thehorizon operations, i.e. beyond the range of standard acoustical tracking technologies. In either circumstance, at the end of its pre-programmed mission, the AUV fails to return to the base station. Such an eventuality defines the need for reliable, long-range acoustic tracking capability that is able to coarsely localize the AUV and subsequently enable communications and/or recovery of the AUV. The thesis describes a novel approach for an acoustic positioning system for AUV localization in harsh environments with non-standard acoustic challenges that can be implemented using only basic acoustic technology, a basic singlebeacon, single-hydrophone (SBSH) system. Inversive geometric techniques are applied for source localization of a one-way traveling, asynchronous acoustic signal. This differs from the usual methods of spherical, two-way direct flight measurement based on time of arrival (TOA), or hyperbolic, one-way time difference of arrival (TDOA) target tracking for transmission based on a purely Euclidean geometry. This is a novel approach to the problem of localizing an AUV. A second method of solving the non-linear system of equations that arise from the problem using the SBSH approach is derived. Both methods, the novel Apollonian inversion geometry localization (AIGL) and the non linear system localization (NLSL), are evaluated in simulation and using live field data. It will be shown that the novel algorithm performs comparable to the standard method of solving the nonlinear systems resulting from a SBSH approach. Furthermore, in certain situations it improves the localization result.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".