An underwater acoustic target recognition method combining wavelet decomposition and an improved convolutional neural network

被引：0
|
作者：
Huang Q. [1 ]
Zeng X. [1 ]
机构：
[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2022年 / 43卷 / 02期
关键词：
Adam gradient optimization; Batch normalization; Convolutional neural network; Deep learning; Empirical mode decomposition; Ship radiated noise; Underwater target recognition; Wavelet decomposition;
D O I：
10.11990/jheu.202011040
中图分类号：
学科分类号：
摘要：
An improved convolutional neural network (CNN) combined with wavelet decomposition was developed for the classification and recognition of underwater acoustic signals with non-stationary characteristics. An underwater target recognition algorithm called WAVEDEC_CNN was developed and verified using four types of collected lake test data. Compared with the traditional MFCC+SVM method, the WAVEDEC_CNN algorithm increased the correct recognition rate by 15.38%. Additionally, compared with the NO_CNN, WPDEC _CNN and EMD _CNN methods, the correct recognition rate of the WAVEDEC_CNN algorithm was increased by 4.41%, 3.23%, 12.81%, respectively. Furthermore, the proposed WAVEDEC_CNN algorithm had the shortest calculation time compared with the other methods. These results show that the proposed method can be effectively applied in underwater acoustic target recognition. Copyright ©2022 Journal of Harbin Engineering University.
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页码：159 / 165
页数：6
相关论文
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