Multisensor modeling underwater with uncertain information Stewart, W. Kenneth 2011-09-14T18:24:18Z 2011-09-14T18:24:18Z 1988-07-05
dc.description Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution July 5, 1988 en_US
dc.description.abstract This thesis develops an approach to the construction of multidimensional stochastic models for intelligent systems exploring an underwater environment. The important characteristics shared by such applications are: real-time constraints: unstructured, three-dimensional terrain; high-bandwidth sensors providing redundant, overlapping coverage; lack of prior knowledge about the environment; and inherent inaccuracy or ambiguity in sensing and interpretation. The models are cast as a three-dimensional spatial decomposition of stochastic, multisensor feature vectors that describe an underwater environment. Such models serve as intermediate descriptions that decouple low-level, high-bandwidth sensing from the higher-level, more asynchronous processes that extract information. A numerical approach to incorporating new sensor information--stochastic backprojection--is derived from an incremental adaptation of the summation method for image reconstruction. Error and ambiguity are accounted for by blurring a spatial projection of remote-sensor data before combining it stochastically with the model. By exploiting the redundancy in high-bandwidth sensing, model certainty and resolution are enhanced as more data accumulate. In the case of three-dimensional profiling, the model converges to a "fuzzy" surface distribution from which a deterministic surface map is extracted. Computer simulations demonstrate the properties of stochastic backprojection and stochastic models. Other simulations show that the stochastic model can be used directly for terrain-relative navigation. The method is applied to real sonar data sets from multibeam bathymetric surveying (Sea Beam), towed sidescan bathymetry (Sea MARC II), towed sidescan acoustic imagery (Sea MARC I & II), and high-resolution scanning sonar aboard a remotely operated vehicle. A multisensor application combines Sea Beam bathvmetry and Sea MARC I intensity models. Targeted real-time applications include shipboard mapping and survey, a piloting aid for remotely operated vehicles and manned submersibles, and world modeling for autonomous vehicles. en_US
dc.description.sponsorship Principal funding for this research was provided by the Sea Grant Program of the Massachusetts Institute of Technology. My course work and early research were supported by a graduate fellowship from the Office of Naval Research. Other significant help has come from the Monitor Marine Sanctuary Program of the National Oceanic and Atmospheric Administration. en_US
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dc.identifier.citation Stewart, W. K. (1988). Multisensor modeling underwater with uncertain information [Doctoral thesis, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution]. Woods Hole Open Access Server.
dc.identifier.doi 10.1575/1912/4809
dc.language.iso en_US en_US
dc.publisher Massachusetts Institute of Technology and Woods Hole Oceanographic Institution en_US
dc.relation.ispartofseries WHOI Theses en_US
dc.subject Stochastic analysis en_US
dc.subject Scanning systems en_US
dc.title Multisensor modeling underwater with uncertain information en_US
dc.type Thesis en_US
dspace.entity.type Publication
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relation.isAuthorOfPublication.latestForDiscovery a01eb0e9-0843-4160-8075-9a85f0ee02fb
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