Acoustic localization of an unknown number of sources in an uncertain ocean environment
Mots-clés :
Acoustic noise, Acoustics, Simulated annealing, Acoustic data, Acoustic localization, Bayesian formulation, Bayesian information criterion, Environmental parameter, Gibbs sampling, Hybrid optimization, Implicit formulation, Interfering sources, Multiple source, Noise statistics, Noise variance, Ocean acoustics, Ocean environment, Prior information, Shallow-water, Source location, Source strengthRésumé
Cet article developpe une nouvelle approche de localisation simultanee d’un nombre inconnu de sources acoustiques sous-marine lorsque les proprietes de l’environnement de l’ocean sont mal connues, fondee sur la minimisation du Critere d’information Baysien (CIB) sur la source et les parametres environnementaux. Une formulation Bayesienne est developpe pour que les parametres de la colonne d’eau et des fonds marins, les statistiques du bruit, et le nombre, lieux et points forts complexes (amplitude et phase) de multiples sources sont consideré comme variables inconnues aratoires forcee par les donnees acoustiques et information préexistante. Le CIB, qui stabilise les résultats inadapte avec une penalite pour les parametres errones, est minimise en utilisant l’optimisation adaptative hybrides simulation d’annelage adaptif simplex pour les parametres environnementaux et l’echantillonnage de Gibbs pour les endroits de source. Des expressions de vraisemblance-maximal pour les intensites de source et la variance de bruit à chaque frequence permet les parametres à etre echantillonnes implicitement, en réduisant la dimensionnalite de l’inversion. L’echantillonnage de Gibbs et la formulation implicite fournis une plateforme efficace pour l’ajout et la suppression des sources lors de l’optimisation. Un exemple simule est présente qui considere la localisation d’une source tranquille immerge dans la presence de deux sources d’interfrence forte.
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