CLASSIFICATION OF SURFACE VESSELS USING UNDERWATER ACOUSTIC DATA AND MACHINE LEARNING
| dc.contributor.advisor | Fargues, Monique P. | |
| dc.contributor.advisor | Gemba, Kay L. | |
| dc.contributor.author | Henderson, John M. | |
| dc.contributor.department | Electrical and Computer Engineering (ECE) | |
| dc.date.accessioned | 2023-10-31T17:15:07Z | |
| dc.date.available | 2023-10-31T17:15:07Z | |
| dc.date.issued | 2023-09 | |
| dc.description.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. | en_US |
| dc.description.distributionstatement | Approved for public release. Distribution is unlimited. | en_US |
| dc.description.service | Lieutenant, United States Navy | en_US |
| dc.identifier.curriculumcode | 590, Electronic Systems Engineering | |
| dc.identifier.thesisid | 39194 | |
| dc.identifier.uri | https://hdl.handle.net/10945/72356 | |
| dc.publisher | Monterey, CA; Naval Postgraduate School | en_US |
| dc.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. | en_US |
| dc.subject.author | machine learning | en_US |
| dc.subject.author | AI | en_US |
| dc.subject.author | SONAR | en_US |
| dc.subject.author | ShipsEar | en_US |
| dc.subject.author | ML | en_US |
| dc.subject.author | GMM | en_US |
| dc.title | CLASSIFICATION OF SURFACE VESSELS USING UNDERWATER ACOUSTIC DATA AND MACHINE LEARNING | en_US |
| dc.type | Thesis | en_US |
| dspace.entity.type | Publication | |
| etd.thesisdegree.discipline | Electrical Engineering | en_US |
| etd.thesisdegree.grantor | Naval Postgraduate School | en_US |
| etd.thesisdegree.level | Masters | en_US |
| etd.thesisdegree.name | Master of Science in Electrical Engineering | en_US |
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