Deep learning-based DOA estimation using CRNN for underwater acoustic arrays

Deep learning-based DOA estimation using CRNN for underwater acoustic arrays

In the marine environment, estimating the direction of arrival (DOA) is challenging because of the multipath signals and low signal-to-noise ratio (SNR). In this paper, we propose a convolutional recurrent neural network (CRNN)-based method for underwater DOA estimation using an acoustic array. The...

Full description

Bibliographic Details
Main Authors: Xiaoqiang Li, Jianfeng Chen, Jisheng Bai, Muhammad Saad Ayub, Dongzhe Zhang, Mou Wang, Qingli Yan
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-11-01
Series:Frontiers in Marine Science
Subjects:
DOA estimation
array signal processing
underwater acoustic
convolutional recurrent neural network
deep learning
Online Access:https://www.frontiersin.org/articles/10.3389/fmars.2022.1027830/full

Internet

https://www.frontiersin.org/articles/10.3389/fmars.2022.1027830/full

Similar Items