Underwater target classification using deep learning

Underwater target classification is an important research direction in sonar signal processing. There are two core parts in this area: feature extraction and classifier design. In this paper, a classifier based on time delay neural network (TDNN) was proposed, which has great advantages of modeling the temporal dynamics and representing the complex nonlinear relationship. Filter bank (FBANK) is used to extract features containing spectrum information as the input of the network. The proposed TDNN based classifier is evaluated on simulated data and real experimental data separately. The experimental results showed that the proposed method has higher classification accuracy than traditional methods such as support vector machine (SVM). Additionally, more details are compared in different feature dimensions and different SNRs. The method we proposed is also verified in real environmental data. More than 90% accuracy is achieved in three-classification experiment in real environment.