4.7 Article

Deep convolution stack for waveform in underwater acoustic target recognition

期刊

SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41598-021-88799-z

关键词

-

资金

  1. Ocean Networks Canada
  2. National Natural Science Foundation of China [61673085]
  3. Fundamental Research for the Central Universities [ZYGX2019J074]
  4. Science Strength Promotion Programme of UESTC [Y03111023901014006]

向作者/读者索取更多资源

In this paper, a novel multiscale residual deep neural network (MSRDN) is proposed for underwater acoustic target recognition, which achieves good recognition accuracy by introducing multiscale residual units (MSRU) to construct the network framework.
In underwater acoustic target recognition, deep learning methods have been proved to be effective on recognizing original signal waveform. Previous methods often utilize large convolutional kernels to extract features at the beginning of neural networks. It leads to a lack of depth and structural imbalance of networks. The power of nonlinear transformation brought by deep network has not been fully utilized. Deep convolution stack is a kind of network frame with flexible and balanced structure and it has not been explored well in underwater acoustic target recognition, even though such frame has been proven to be effective in other deep learning fields. In this paper, a multiscale residual unit (MSRU) is proposed to construct deep convolution stack network. Based on MSRU, a multiscale residual deep neural network (MSRDN) is presented to classify underwater acoustic target. Dataset acquired in a real-world scenario is used to verify the proposed unit and model. By adding MSRU into Generative Adversarial Networks, the validity of MSRU is proved. Finally, MSRDN achieves the best recognition accuracy of 83.15%, improved by 6.99% from the structure related networks which take the original signal waveform as input and 4.48% from the networks which take the time-frequency representation as input.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据