Underwater acoustic target recognition using line enhancement and deep neural network

被引：0
作者：
Yu X. [1 ,2 ,3 ]
Chi C. [1 ,3 ]
Li S. [1 ,3 ]
Li D. [1 ,2 ,3 ]
机构：
[1] Institute of Acoustics, Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
[3] Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Sciences, Beijing
来源：
Shengxue Xuebao/Acta Acustica | 2024年 / 49卷 / 04期
关键词：
Deep feature; Line enhancement; Neural network; Underwater acoustic target recognition;
D O I：
10.12395/0371-0025.2024021
中图分类号：
学科分类号：
摘要：
To enhance the features of underwater acoustic target signals, and improve the performance of underwater acoustic target recognition based on deep neural network, a target recognition method using line enhancement and deep neural network is proposed. This method focuses on the narrowband information enhancement and sets an adaptive line enhancement filter at the front end of the VGGish network. The signals are processed by the line enhancement filter and input into the network to extract deep features, and then these features are classified by a classifier. The effectiveness of the method is verified by the actual underwater acoustic dataset. Principal component analysis is performed on the deep feature set of the underwater acoustic signals, and the results show that the compactness of the deep feature set obtained after line enhancement is significantly improved. The proposed method can obtain a recognition accuracy of 94.83% on the test dataset, which is improved by 5.48% compared to the case without line enhancement, and it is also more robust under the condition of low signal-to-noise ratio. © 2024 Science Press. All rights reserved.
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页码：656 / 663
页数：7
相关论文
共 22 条
[1]  
31, (2012)
[2]  
35, (2019)
[3]  
44
[4]  
Barros R E, Ebecken N F F., Development of a ship classification method based on convolutional neural network and cyclostationarity analysis, Mech. Syst. Signal Process, 170, (2022)
[5]  
40
[6]  
Doan V S, Thien H T, Kim D S., Underwater acoustic target classification based on dense convolutional neural network, IEEE Geosci, 19, pp. 1-5, (2022)
[7]  
Li C, Huang Z Q, Xu J, Et al., Underwater target classification using deep learning, OCEANS 2018, (2018)
[8]  
Zhang Q, Da L L, Zhang Y H, Et al., Integrated neural networks based on feature fusion for underwater target recognition, Appl. Acoust, 182, 4, (2021)
[9]  
Yue H, Zhang L L, Wang D Z, Et al., The classification of underwater acoustic targets based on deep learning methods, 2nd International Conference on Control, Automation and Artificial Intelligence, pp. 408-411, (2017)
[10]  
33, (2008)
123