Ji Fang, Lu Shaoqing, Ni Junshuai, Li Ziming, Feng Weijia
China Ship Research and Development Academy, Beijing 100101, China.
Sensors (Basel). 2025 Apr 18;25(8):2573. doi: 10.3390/s25082573.
In order to improve the efficiency of the deep network model in processing the radiated noise signals of underwater acoustic targets, this paper introduces a Singular Spectrum Analysis and Channel Attention Convolutional Neural Network (SSA-CACNN) model. The front end of the model is designed as an SSA filter, and its input is the time-domain signal that has undergone simple preprocessing. The SSA method is utilized to separate the noise efficiently and reliably from useful signals. The first three orders of useful signals are then fed into the CACNN model, which has a convolutional layer set up at the beginning of the model to further remove noise from the signal. Then, the attention of the model to the feature signal channels is enhanced through the combination of multiple groups of convolutional operations and the channel attention mechanism, which facilitates the model's ability to discern the essential characteristics of the underwater acoustic signals and improve the target recognition rate. Experimental Results: The signal reconstructed by the first three-order waveforms at the front end of the SSA-CACNN model proposed in this paper can retain most of the features of the target. In the experimental verification using the ShipsEar dataset, the model achieved a recognition accuracy of 98.64%. The model's parameter count of 0.26 M was notably lower than that of other comparable deep models, indicating a more efficient use of resources. Additionally, the SSA-CACNN model had a certain degree of robustness to noise, with a correct recognition rate of 84.61% maintained when the signal-to-noise ratio (SNR) was -10 dB. Finally, the pre-trained SSA-CACNN model on the ShipsEar dataset was transferred to the DeepShip dataset with a recognition accuracy of 94.98%.
为了提高深度网络模型处理水下声学目标辐射噪声信号的效率，本文介绍了一种奇异谱分析与通道注意力卷积神经网络（SSA-CACNN）模型。该模型前端设计为SSA滤波器，其输入是经过简单预处理的时域信号。利用SSA方法从有用信号中高效可靠地分离出噪声。然后将有用信号的前三个阶次输入到CACNN模型中，该模型在开始处设置了卷积层以进一步去除信号中的噪声。接着，通过多组卷积运算与通道注意力机制相结合，增强模型对特征信号通道的注意力，这有助于模型辨别水下声学信号的本质特征并提高目标识别率。实验结果：本文提出的SSA-CACNN模型前端由前三阶波形重建的信号能够保留目标的大部分特征。在使用ShipsEar数据集的实验验证中，该模型实现了98.64%的识别准确率。该模型的参数数量为0.26M，明显低于其他可比深度模型，表明资源利用效率更高。此外，SSA-CACNN模型对噪声具有一定的鲁棒性，当信噪比（SNR）为-10dB时，正确识别率保持在84.61%。最后，在ShipsEar数据集上预训练的SSA-CACNN模型迁移到DeepShip数据集上，识别准确率为94.98%。
