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 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 | 0.000 | 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 | 0.000 |
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".