Array element localization accuracy and survey design
Bibliographic record
Abstract
Accurate localization of the individual elements of an underwater acoustic receiver array is an important prerequisite to advanced array processing applications. Array element localization (AEL) methods are typically based on inverting acoustic arrival-time measurements from controlled sources at (approximately) known positions to the receivers to be localized. This paper presents and illustrates a general approach to AEL inversion and to AEL survey design based on quantifying the posterior receiverlocation uncertainty, taking into account uncertainties in the data, source locations, sound speed, and water depth. The inversion is based on a fast ray-tracing algorithm that employs Newton's method and the method of images to determine eigenrays for direct and reflected arrivals. The efficiency of this approach allows computationally intensive analysis such as Monte-Carlo appraisal and nonlinear optimization for designing optimal source configurations. These algorithms provide a rigorous approach that can be applied to examine all aspects of AEL accuracy and survey design, illustrated here by several examples. It is shown that synchronized AEL surveys (in which source transmission times are known) provide only a minor improvement over non-synchronized surveys (often much simpler logistically), and the difference can be made up by using more sources in an optimal configuration or by including additional arrivals. Including multiple-reflected arrivals improves receiver depth estimates (provided water depth is well known), but provides little improvement in horizontal localization.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".