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dc.contributor.advisorTimothy K. Stanton, Andone C. Lavery, and Peter L. Tyack.en_US
dc.contributor.authorLee, Wu-Jungen_US
dc.contributor.otherWoods Hole Oceanographic Institution.en_US
dc.date.accessioned2013-06-17T19:53:25Z
dc.date.available2013-06-17T19:53:25Z
dc.date.copyright2013en_US
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/79288
dc.descriptionThesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 213-229).en_US
dc.description.abstractThe interpretation of echoes collected by active remote-sensing systems, such as sonar and radar, is often ambiguous due to the complexities in the scattering processes involving the scatterers, the environment, and the sensing system. This thesis addresses this challenge using a combination of laboratory and field experiments, theoretical modeling, and numerical simulations in the context of acoustic scattering by marine organisms. The unifying themes of the thesis are 1) quantitative characterization of the spectral, temporal, and statistical features derived from echoes collected using both broadband and narrowband signals, and 2) the interpretation of echoes by establishing explicit links between echo features and the sources of scattering through physics principles. This physics-based approach is distinct from the subjective descriptions and empirical methods employed in most conventional fisheries acoustic studies. The first part focuses on understanding the dominant backscattering mechanisms of live squid as a function of orientation. The study provides the first broadband backscattering laboratory data set from live squid at all angles of orientation, and conclusively confirms the fluid-like, weakly-scattering material properties of squid through a series of detailed comparisons between data and predictions given by models derived based on the distorted-wave Born approximation. In the second part, an exact analytical narrowband model and a numerical broadband model are developed based on physics principles to describe the probability density function of the amplitudes of echo envelopes (echo pdf) of arbitrary aggregations of scatterers. The narrowband echo pdf model significantly outperforms the conventional mixture models in analyzing simulated mixed assemblages. When applied to analyze fish echoes collected in the ocean, the numerical density of fish estimated using the broadband echo pdf model is comparable to the density estimated using echo integration methods. These results demonstrate the power of the physics-based approach and give a first-order assessment of the performance of echo statistics methods in echo interpretation. The new data, models, and approaches provided here are important for advancing the field of active acoustic observation of the ocean.en_US
dc.description.statementofresponsibilityby Wu-Jung Lee.en_US
dc.format.extent229 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.subjectJoint Program in Applied Ocean Science and Engineering.en_US
dc.subjectWoods Hole Oceanographic Institution.en_US
dc.titleBroadband and statistical characterization of echoes from random scatterers : application to acoustic scattering by marine organismsen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentJoint Program in Applied Ocean Physics and Engineeringen_US
dc.contributor.departmentWoods Hole Oceanographic Institutionen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.identifier.oclc846911716en_US


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