A Survey of Underwater Acoustic Target Recognition Methods Based on Machine Learning

Underwater acoustic target recognition (UATR) technology has been implemented widely in the fields of marine biodiversity detection, search and rescue, seabed mapping, providing an essential basis for human economic military activities. With rapid development machine-learning-based acoustics field, these methods receive wide attention display a potential impact on UATR problems. This paper reviews current based machine learning. We focus mostly, but not solely, target-radiated noise from passive sonar. First, we provide overview underwater acquisition process briefly introduce classical signal feature extraction methods. In this paper, are classified learning algorithms used as technologies using statistical methods, deep models, transfer data augmentation UATR. Finally, challenges method summarized directions future put forward.