Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM

Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM

In recent years, single vector hydrophones have attracted widespread attention in target direction estimation due to their compact design and advantages in complex underwater acoustic environments. However, traditional direction of arrival (DOA) estimation algorithms often struggle to maintain high...

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Bibliographic Details
Main Authors: Fanyu ZENG, Yaning HAN, Hongyuan YANG, Dapeng YANG, Fan ZHENG
Format: Article
Language:English
Published: Institute of Fundamental Technological Research Polish Academy of Sciences 2025-06-01
Series:Archives of Acoustics
Subjects:
single vector hydrophone
direction of arrival (doa)
convolutional neural network (cnn)
convolutional block attention module (cbam)
noise resistance
Online Access:https://acoustics.ippt.pan.pl/index.php/aa/article/view/4138
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