DNN and improved K-means based ship noise open set recognition

In order to improve the performance of ship noise recognition system and realize open set recognition, an open set recognition method of ship radiation noise based on Deep Neural Network (DNN) and improved K-means is proposed. First, the Welch power spectrum estimation method is used to extract the characteristics of ship radiation noise. Then, the deep neural network model is designed for further extraction of feature vectors. Finally, the improved K-means model is used to realize open set recognition. Experiments are carried out on the measured data, and the results show that the proposed method can realize the open set recognition of ship radiation noise. The average recognition accuracy for the measured data is 93.5%, which is 6.2% higher than that of the DNN+K-means++ method. After adding experimental ship engine noise or fishing boat noise to the measured data, the experimental results show that the recognition method has good robustness under the interference of other ship noises.