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Spectral feature classification of oceanographic processes using an autonomous underwater vehicle

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dc.contributor.advisor Arthur B. Baggeroer and James G. Bellingham. en_US
dc.contributor.author Zhang, Yanwu en_US
dc.contributor.other Joint Program in Applied Ocean Science and Engineering. en_US
dc.date.accessioned 2005-09-27T20:17:23Z
dc.date.available 2005-09-27T20:17:23Z
dc.date.copyright 2000 en_US
dc.date.issued 2000 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/29047
dc.description Thesis (Ph.D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Ocean Engineering/Woods Hole Oceanographic Institution), 2000. en_US
dc.description Includes bibliographical references (leaves 202-211). en_US
dc.description.abstract The thesis develops and demonstrates methods of classifying ocean processes using an underwater moving platform such as an Autonomous Underwater Vehicle (AUV). The "mingled spectrum principle" is established which concisely relates observations from a moving platform to the frequency-wavenumber spectrum of the ocean process. It clearly reveals the role of the AUV speed in mingling temporal and spatial information. For classifying different processes, an AUV is not only able to jointly utilize the time-space information, but also at a tunable proportion by adjusting its cruise speed. In this respect, AUVs are advantageous compared with traditional oceanographic platforms. Based on the mingled spectrum principle, a parametric tool for designing an AUVbased spectral classifier is developed. An AUV's controllable speed tunes the separability between the mingled spectra of different processes. This property is the key to optimizing the classifier's performance. As a case study, AUV-based classification is applied to distinguish ocean convection from internal waves. The mingled spectrum templates are derived from the MIT Ocean Convection Model and the Garrett-Munk internal wave spectrum model. To allow for mismatch between modeled templates and real measurements, the AUVbased classifier is designed to be robust to parameter uncertainties. By simulation tests on the classifier, it is demonstrated that at a higher AUV speed, convection's distinct spatial feature is highlighted to the advantage of classification. Experimental data are used to test the AUV-based classifier. An AUV-borne flow measurement system is designed and built, using an Acoustic Doppler Velocimeter (ADV). The system is calibrated in a high-precision tow tank. In February 1998, the AUV acquired field data of flow velocity in the Labrador Sea Convection Experiment. The Earth-referenced vertical flow velocity is extracted from the raw measurements. The classification test result detects convection's occurrence, a finding supported by more traditional oceanographic analyses and observations. The thesis work provides an important foundation for future work in autonomous detection and sampling of oceanographic processes. en_US
dc.description.statementofresponsibility by Yanwu Zhang. en_US
dc.format.extent 211 leaves en_US
dc.format.extent 13103324 bytes
dc.format.extent 13103082 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Ocean Engineering. en_US
dc.subject /Woods Hole Oceanographic Institution. Joint Program in Applied Ocean Science and Engineering. en_US
dc.title Spectral feature classification of oceanographic processes using an autonomous underwater vehicle en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Ocean Engineering. en_US
dc.contributor.department Joint Program in Applied Ocean Science and Engineering. en_US
dc.contributor.department Woods Hole Oceanographic Institution. en_US
dc.identifier.oclc 47942129 en_US


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