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.
| 投稿的翻译标题 | An underwater acoustic target recognition method combining wavelet decomposition and an improved convolutional neural network |
|---|---|
| 源语言 | 繁体中文 |
| 页（从-至） | 159-165 |
| 页数 | 7 |
| 期刊 | Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University |
| 卷 | 43 |
| 期 | 2 |
| DOI | |
| 出版状态 | 已出版 - 5 2月 2022 |
关键词
- Adam gradient optimization
- Batch normalization
- Convolutional neural network
- Deep learning
- Empirical mode decomposition
- Ship radiated noise
- Underwater target recognition
- Wavelet decomposition