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 différentes méthodes de décomposition modale utilisées en matched-mode process ing (MMP) pour la localisation de source acoustique marine.La méthode MMP consiste à décomposer le champ acoustique mesuré par un réseau de capteurs pour obtenir les excitations modales présentes (décomposition modale), et ensuite à faire correspondre ces excitations avec les excitations calculées par un modèle numérique pour une grille de positions possibles de la source.Le problème de décomposition modale peut être mal posé et instable si les capteurs du réseau ne fournissent pas un échantillonnage spatial adéquate du champ acoustique.Les résultants de différentes approaches peuvent donc varier considérablement.Les solutions peuvent être caractérisées par la résolution modale et la covariance de la solution.Cependant, le critère ultime d'utilité des différentes méthodes est de savoir leur degré de perfomance dans l'algorithme MMP pour la localisation de source.Dans cet article, la résolution et la variance des méthodes sont examinées en utilisant un environnement océanique idéal.Les résultats obtenus avec la méthode MMP sont comparés pour une série de cas réalistique et synthétiques, com prenant différents niveaux de bruit et différentes configurations du réseau de capteurs.Les meilleurs résultats sont obtenus avec la méthode d'inversion basée sur une régularisation d'ordre zéro.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".