Present status and challenges of underwater acoustic target recognition technology: A review

• Published in: Frontiers in physics 2022-10, Vol.10
• Main Authors: Zhufeng, Lei, Xiaofang, Lei, Qingyang, Zhang
• Format: Article
• Language: English
• Subjects: feature extraction; feature selection; machine learning; radiated noise recognition; underwater acoustic target
• ISSN: 2296-424X; 2296-424X
• DOI: 10.3389/fphy.2022.1044890

Future naval warfare has placed high demands on underwater targets’ target detection, target recognition, and opposition resistance, among other things. However, the ocean’s complex underwater acoustic environment and the evolving “stealth” technology of underwater targets pose significant challenges to target detection systems, which has become a hot topic in the field of underwater acoustic signal processing in various countries. This study introduced the mechanism of underwater target radiation noise generation, analyzed the research progress and development of underwater target radiation noise recognition by applying machine learning from three perspectives: signal acquisition, feature extraction, and signal recognition at home and abroad, and elaborated on the challenges of underwater target-radiated noise recognition technology against the backdrop of rapid computing science development, and finally, an integrated signal processing method based on the fusion of traditional feature extraction methods and deep learning is proposed for underwater target radiation noise recognition, which improves the low recognition rate of traditional methods and also circumvents the problem of deep learning requiring high computational cost, and is an important direction for future hydroacoustic signal processing.