Acoustic localization of an unknown number of sources in an uncertain ocean environment
Bibliographic record
Abstract
This paper develops a new approach to simultaneous localization of an unknown number of ocean acoustic sources when properties of the environment are poorly known, based on minimizing the Bayesian information criterion (BIC) over source and environmental parameters. A Bayesian formulation is developed in which water-column and seabed parameters, noise statistics, and the number, locations, and complex strengths (amplitudes and phases) of multiple sources are considered unknown random variables constrained by acoustic data and prior information. The BIC, which balances data misfit with a penalty for extraneous parameters, is minimized using hybrid optimization (adaptive simplex simulated annealing) over environmental parameters and Gibbs sampling over source locations. Closed- form maximum-likelihood expressions for source strength and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality of the inversion. Gibbs sampling and the implicit formulation provide an efficient scheme for adding and deleting sources during the optimization. A simulated example is presented which considers localizing a quiet submerged source in the presence of two loud interfering sources in a poorly-known shallow-water environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".