Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

• Main Authors: Haesang Yang, Keunhwa Lee, Youngmin Choo, Kookhyun Kim
• Format: Article
• Language: English
• Published: The Korean Society of Ocean Engineers 2020-06-01
• Series: 한국해양공학회지
• Subjects: underwater acoustics; passive sonar system; machine learning; deep learning; signal processing; passive target classification; passive source localization
• Online Access: https://doi.org/10.26748/KSOE.2020.017

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.