Automated open circuit scuba diver detection with low cost passive sonar and machine learning

This thesis evaluates automated open-circuit scuba diver detection using low-cost passive sonar and machine learning. Previous automated passive sonar scuba diver detection systems required matching the frequency of diver breathing transients to that of an assumed diver breathing frequency. Earlier work required prior knowledge of both the number of divers and their breathing rate. Here an image processing approach is used for automated diver detection by implementing a deep convolutional neural network. Image processing was chosen because it is a proven method for sonar classification by trained human operators. The system described here is able to detect a scuba diver from a single acoustic emission from the diver. Twenty dives were conducted in support of this work at the WHOI pier from October 2018 to February 2019. The system, when compared to a trained human operator, correctly classified approximately 93% of the data. When sequential processing techniques were applied, system accuracy rose to 97%. This demonstrated that a combination of low-cost, passive sonar and a properly tuned convolutional neural network can detect divers in a noisy environment to a range of at least 12.49 m (50 feet).

Name thesis:degree_name

Master

Department dc:contributor.department

Joint Program in Applied Ocean Science and Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Cole, Andrew M.,Lieutenant Commander.

Advisor dc:contributor.advisor

• Carl L. Kaiser and Andone C. Lavery.

• Joint Program in Applied Ocean Science and Engineering.
• Mechanical Engineering.
• Woods Hole Oceanographic Institution.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122269

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122269