Underwater acoustic target classification based on u-shaped network

Abstract

Target recognition is a key task and a difficult technique in underwater acoustic signal processing. One of the most challenging problem is that the label information of the underwater acoustic samples is scarce or missing. To solve the problem, this paper presents a local skip connection u-shaped architecture network(U-Net)based on the convolutional neural network(CNN).To this end, the network architecture is designed cleverly to generate a contracting path and an expansive path to achieve the extraction of different scale features. More importantly, a local skip connection mechanism is proposed to optimize classification rates by reusing former feature maps in contracting path. The experimental results of the measured dataset demonstrate the recognition accuracy of the model is better than that of deep belief network(DBN) and generative adversarial network(GAN) networks.Further research on three kinds of network by visualization method shows that the proposed network can learn more effective feature information with limited samples.