Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder

Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder

Abstract Passive localization of underwater targets was a thorny problem in underwater acoustics. For traditional model-driven passive localization methods, the main challenges are the inevitable environmental mismatch and the presence of interference and noise everywhere. In recent years, data-driv...

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Bibliographic Details
Main Authors: Pian Jin, Biao Wang, Lebo Li, Peng Chao, Fangtong Xie
Format: Article
Language:English
Published: SpringerOpen 2022-11-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Underwater acoustic source localization
Semi-supervised learning
Convolutional autoencoder
Self-attentive mechanism
Data mismatch
Online Access:https://doi.org/10.1186/s13634-022-00941-9

Internet

https://doi.org/10.1186/s13634-022-00941-9

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