Abstract:In underwater acoustic research, the recognition and classification of ship targets is a very important issue. Ship radiated noise contains a large number of features, which can identify and classify ship targets. Firstly, this paper introduces four underwater acoustic target radiation noise databases. These databases collect a large amount of data and record the collected information completely. They include not only ship targets, but also underwater targets such as marine organisms and environmental noise, which can provide data support for researchers with different needs. Secondly, this paper summarizes the commonly used ship radiated noise feature extraction technology in the field of underwater acoustics. From the three directions of line spectrum recognition, wavelet analysis and nonlinear recognition, the current mature ship radiated noise recognition technology is summarized. Finally, the paper analyzes the technical development of ship radiated noise recognition from the two directions of traditional recognition technology and deep learning. The traditional recognition technology mainly involves support vector machine, cluster analysis and other technologies. Deep learning is a rapidly developing recognition technology in recent years, and it is a hot research direction of ship target recognition. This paper aims to provide a comprehensive overview of research status and development trends for researchers of ship target classification and recognition.
