基于改进小波阈值去噪的深度学习水下目标分类

由于海洋环境噪声复杂，噪声等级高，水下待识别目标信噪比低，从而造成了特征提取困难，目标识别率低的问题。基于此，文章提出了基于改进小波阈值的深度学习水下目标分类方法。此方法在传统小波阈值去噪的基础上提出了一种新的小波阈值函数，对于所采用的具体阈值将其与分解尺度相联系，从而实现降低背景噪声，提升水下目标分类识别率的目的。此方法对实测舰船辐射噪声信号进行小波分解，提取每一层的高频小波系数并对其进行处理；对处理完的信号再提取时频特征，最后将其输入后续的深度学习网络中。实验结果发现：在利用原有数据集情况下，利用基于改进小波阈值的深度学习进行水下目标的分类识别，采用卷积神经网络算法可达到88.56%的分类识别率。对前述实验结果进一步分析后，采用生成对抗网络的方法扩充数据样本，可达到96.673%的分类识别率。

Deep learning underwater target classification based on improved wavelet threshold denoising

Because of the complex marine ambient noise and low signal to noise ratio of the underwater targets to be identified, it is difficult to extract target features, and the target recognition rate is low. Aiming at this problem, a deep learning underwater target recognition method based on improved wavelet threshold is proposed in this paper. In this method, a new wavelet threshold function based on the traditional wavelet threshold denoising method is adopted. The specific threshold relates to the decomposition scale, so as to reduce the background noise and improve the recognition rate of underwater targets. This method performs the wavelet decomposition of the measured ship radiated noise signal and extracts the high-frequency wavelet coefficients of each layer for processing. The time-frequency characteristics of the processed signal are extracted and input it into the subsequent deep learning neural network. The experimental results find that, for the original data set, the deep learning underwater target recognition method based on the improved wavelet threshold can bring the recognition rate of convolutional neural network (CNN) to 88.56%. Further analysis shows that by using a generative adversarial network (GAN), the data samples can be expanded to reach a recognition rate of 96.673%.