Through-Ice Acoustic Source Tracking Using Vision Transformers with Ordinal Classification

Ice environments pose challenges for conventional underwater acoustic localization techniques due to their multipath and non-linear nature. In this paper, we compare different deep learning networks, such as Transformers, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks,...

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Detalles Bibliográficos
Autores principales: Whitaker, Steven, Barnard, Andrew, Anderson, George D., Havens, Timothy C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269127/
https://www.ncbi.nlm.nih.gov/pubmed/35808200
http://dx.doi.org/10.3390/s22134703

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269127/
https://www.ncbi.nlm.nih.gov/pubmed/35808200
http://dx.doi.org/10.3390/s22134703

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