{"id":{"repo_id":"woods-hole","oai_identifier":"oai:darchive.mblwhoilibrary.org:1912/4809"},"canonical_url":"https://search.dev.ndltd.org/etd/woods-hole/oai:darchive.mblwhoilibrary.org:1912/4809","repository":{"repo_id":"woods-hole","name":"Woods Hole Oceanographic Institute","base_url":"https://darchive.mblwhoilibrary.org/server/oai/request"},"display":{"title":"Multisensor modeling underwater with uncertain information","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Stewart, W. Kenneth"],"institution":"Massachusetts Institute of Technology and Woods Hole Oceanographic Institution","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1988,"date_issued":"1988-07-05","date_published":"1988-07-05","updated_at":"2026-07-27T22:05:14Z","subjects":["Stochastic analysis","Scanning systems"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.1575/1912/4809"],"render_values":[{"text":"10.1575/1912/4809","href":"https://doi.org/10.1575/1912/4809","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1912/4809","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Stewart, W. 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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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Multisensor modeling underwater with uncertain information"]}]}],"canonical_facts":{"dc:creator":["Stewart, W. Kenneth"],"dc:date.accessioned":["2011-09-14T18:24:18Z"],"dc:date.available":["2011-09-14T18:24:18Z"],"dc:date.issued":["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"],"dc:description.abstract":["This thesis develops an approach to the construction of multidimensional stochastic models for intelligent systems exploring an underwater environment. 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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. 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