Open AccessUnderwater acoustic target recognition method based on a joint neural network
Author(s) -
Xing Cheng Han,
Chen-Xi Ren,
Liming Wei,
Yunjiao Bai
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0266425
Subject(s) - underwater , computer science , convolutional neural network , artificial neural network , time delay neural network , pattern recognition (psychology) , artificial intelligence , speech recognition , joint (building) , underwater acoustics , neocognitron , engineering , geology , architectural engineering , oceanography
To improve the recognition accuracy of underwater acoustic targets by artificial neural network, this study presents a new recognition method that integrates a one-dimensional convolutional neural network and a long short-term memory network. This new network framework is constructed and applied to underwater acoustic target recognition for the first time. Ship acoustic data are used as input to evaluate the network performance. A visual analysis of the recognition results is performed. The results show that this method can realize the recognition and classification of underwater acoustic targets. Compared with a single neural network, the relevant indices, such as the recognition accuracy of the joint network are considerably higher. This provides a new direction for the application of deep learning in the field of underwater acoustic target recognition.