Abstract
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.
| Translated title of the contribution | An underwater acoustic target recognition method combining wavelet decomposition and an improved convolutional neural network |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 159-165 |
| Number of pages | 7 |
| Journal | Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University |
| Volume | 43 |
| Issue number | 2 |
| DOIs | |
| State | Published - 5 Feb 2022 |