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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	EURASIP journal on advances in signal processing Ročník 2022; číslo 1; s. 1 - 20
Hlavní autoři:	Jin, Pian, Wang, Biao, Li, Lebo, Chao, Peng, Xie, Fangtong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.12.2022 Springer Springer Nature B.V SpringerOpen
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!