A comparison of modal decomposition algorithms for matched-mode processing
Bibliographic record
Abstract
This paper compares a variety of modal decomposition methods used in matched-mode processing (MMP) for ocean acoustic source localization.MMP consists of decomposing far-field acoustic mea surements at an array of sensors to obtain the constituent mode excitations (modal decomposition), and then matching these excitations with modelled replica excitations computed for a grid of possible source locations.Modal decomposition can be ill-posed and unstable if the sensor array does not pro vide an adequate spatial sampling of the acoustic field, so the results of different approaches can vary substantially.Solutions can be characterized by modal resolution and solution covariance; however the ultimate test of the utility of the various methods is how well they perform as part of a MMP source localization algorithm.In this paper, the resolution and variance of the methods are examined using an ideal ocean environment, and MMP results are compared for a series of realistic synthetic test cases, including a variety of noise levels and sensor array configurations.Zeroth order regularized inversion is found to give the best results. SOM M AIRECet article compare diffrentes mthodes de dcomposition modale utilises en matched-mode process ing (MMP) pour la localisation de source acoustique marine.La mthode MMP consiste dcomposer le champ acoustique mesur par un rseau de capteurs pour obtenir les excitations modales prsentes (dcomposition modale), et ensuite faire correspondre ces excitations avec les excitations calcules par un modle numrique pour une grille de positions possibles de la source.Le problme de dcomposition modale peut tre mal pos et instable si les capteurs du rseau ne fournissent pas un chantillonnage spatial adquate du champ acoustique.Les rsultants de diffrentes approaches peuvent donc varier considrablement.Les solutions peuvent tre caractrises par la rsolution modale et la covariance de la solution.Cependant, le critre ultime d'utilit des diffrentes mthodes est de savoir leur degr de perfomance dans l'algorithme MMP pour la localisation de source.Dans cet article, la rsolution et la variance des mthodes sont examines en utilisant un environnement ocanique idal.Les rsultats obtenus avec la mthode MMP sont compars pour une srie de cas ralistique et synthtiques, com prenant diffrents niveaux de bruit et diffrentes configurations du rseau de capteurs.Les meilleurs rsultats sont obtenus avec la mthode d'inversion base sur une rgularisation d'ordre zro.
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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) | 0.001 | 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".