A hydroacoustic target recognition method based on joint time-frequency and weighted decision
Recognition of hydroacoustic targets has been a hot issue in the field of hydroacoustics due to the complex-ity of the underwater environment.At present,most of the deep learning-based hydroacoustic target recognition methods are based on a single time-domain or frequency-domain signal to extract hydroacoustic features,while ignoring the time-fre-quency complementary information between the two,which can help improve the accuracy of hydroacoustic target recogni-tion.Therefore,this paper proposes a hydroacoustic target recognition method based on joint time-frequency features and weighted decision from both the perspective of time and frequency domains.The method firstly adopts long short term memory network(LSTM)to extract time domain features of hydroacoustic signals for recognition;then adopts two-dimen-sional convolutional neural network(2D-CNN)to extract frequency domain features of hydroacoustic signals for recogni-tion;finally,the recognition results of both are weighted for decision fusion.The effectiveness of the method was verified on the ShipEar dataset,and its recognition accuracy reached 94.13％,which is higher than other existing methods.The method provides a new idea for the development of deep learning-based hydroacoustic target recognition methods.
万方数据
维普
