A classification method of ship radiated noise based on simulation signal of variational auto-encoder

The performance of the classifier is weak when the number of the ship radiated noise samples is insufficient. Aiming at above problem, this paper proposes a classification method of ship radiated noise based on simulation signal of variational auto-encoder (VAE). First, build a VAE model, input the real ship radiated noise signals into the model to generate a large number of VAE simulation signals. Then, extract the typical features of simulation signals, and use these features to pretrain a convolutional neural network (CNN) classification model. Finally extract the typical features of the real signals to be predicted, and use the pretrained CNN to complete the classification. Experimental results show that the classification accuracy of the pretrained CNN model is 6% to 12% higher than that of the non-pretrained CNN model.