Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Underwater Acoustic Research Trends with Machine Learning: Active SONAR Applications

  • Yang, Haesang (Department of Naval Architecture & Ocean Engineering, Seoul National University) ;
  • Byun, Sung-Hoon (Ocean System Engineering Research Division, Korea Research Institute of Ships & Ocean Engineering) ;
  • Lee, Keunhwa (Department of Defense System Engineering, Sejong University) ;
  • Choo, Youngmin (Department of Defense System Engineering, Sejong University) ;
  • Kim, Kookhyun (School of Naval Architecture & Ocean Engineering, Tongmyong University)
  • Received : 2020.03.04
  • Accepted : 2020.04.13
  • Published : 2020.08.31
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Abstract

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

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References

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