CLASSIFICATION OF SURFACE VESSELS USING UNDERWATER ACOUSTIC DATA AND MACHINE LEARNING
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Authors
Henderson, John M.
Subjects
machine learning
AI
SONAR
ShipsEar
ML
GMM
AI
SONAR
ShipsEar
ML
GMM
Advisors
Fargues, Monique P.
Gemba, Kay L.
Date of Issue
2023-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Automatic vessel classification is a highly relevant research topic, particularly for the U.S. Navy. In this study, we consider three machine learning techniques to classify maritime vessels based on their underwater noise: Gaussian mixture models, random forest, and k-nearest neighbors. The ShipsEar database, developed by Santos-Domínguez et al., was used to conduct the study. Mel-frequency cepstrum coefficients were selected for class feature characteristics to compare with previous findings presented by Santos-Domínguez et al. in their publication titled “ShipsEar: An underwater vessel noise database” published in the Applied Acoustics journal, volume 113. Results indicate that all three methods offer a feasible solution to the classification problem. Notably, Gaussian mixture models show significant performance improvements over results achieved by Santos-Domínguez et al.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering (ECE)
Organization
Identifiers
NPS Report Number
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Funding
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.