水下慢速小目标声学识别方法发展现状与展望OA

Underwater low-speed small targets,represented by divers and unmanned undersea vehicles,have become major threats to nearshore military and economic facilities due to their strong concealment,high maneuverability,and significant destructive potential.Their recognition has emerged as a hot topic and a challenging issue in the field of underwater security.This paper focused on three key aspects of acoustic recognition for underwater low-speed small targets:acoustic signal characteristic analysis,feature extraction,and feature classification.It systematically reviewed the current research status,core challenges,and development trends in this field.First,the acoustic signal characteristics of underwater low-speed small targets were analyzed from the perspectives of active echo signals and passive radiated noise.Subsequently,mainstream feature extraction methods were summarized based on active and passive features.Then,two major classification approaches,namely statistical learning and deep learning,were introduced and compared.Following this,the main challenges faced in this field and corresponding countermeasures were discussed.Finally,in light of technological development trends,future research directions were prospected,aiming to provide references for the advancement of underwater low-speed small target recognition technologies.