Model-based convolutional neural network approach to underwater source-range estimation

Model-based convolutional neural network approach to underwater source-range estimation

This paper is part of a special issue on machine learning in acoustics. A model-based convolutional neural network (CNN) approach is presented to test the viability of this method as an alternative to conventional matched-field processing (MFP) for underwater source-range estimation. The networks ar...

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Detalles Bibliográficos
Autores principales: Chen, R., Schmidt, H.
Otros Autores: Massachusetts Institute of Technology. Department of Mechanical Engineering
Formato: Artículo
Lenguaje:English
Publicado: Acoustical Society of America 2024
Acceso en línea:https://hdl.handle.net/1721.1/154268

Internet

https://hdl.handle.net/1721.1/154268

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