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

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-driven machin...

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Veröffentlicht in:	EURASIP journal on advances in signal processing Jg. 2022; H. 1; S. 1 - 20
Hauptverfasser:	Jin, Pian, Wang, Biao, Li, Lebo, Chao, Peng, Xie, Fangtong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2022 Springer Springer Nature B.V SpringerOpen
Schlagworte:	Acoustics Advances in Underwater Signal Processing for SONAR AUV and Communications Applications Coders Computational linguistics Convolutional autoencoder Data mismatch Datasets Engineering Feature extraction Language processing Localization Machine learning Multilayer perceptrons Natural language interfaces Quantum Information Technology Self-attentive mechanism Semi-supervised learning Signal,Image and Speech Processing Sound sources Spintronics Underwater acoustic source localization Underwater acoustics China Underwater acoustic source localization Self-attentive mechanism Semi-supervised learning Convolutional autoencoder Data mismatch
ISSN:	1687-6180, 1687-6172, 1687-6180
Online-Zugang:	Volltext
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