Multisensor modeling underwater with uncertain information
Multisensor modeling underwater with uncertain information
dc.contributor.author | 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 | 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 |
dc.format.mimetype | application/pdf | |
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. https://doi.org/10.1575/1912/4809 | |
dc.identifier.doi | 10.1575/1912/4809 | |
dc.identifier.uri | https://hdl.handle.net/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 | |
relation.isAuthorOfPublication | a01eb0e9-0843-4160-8075-9a85f0ee02fb | |
relation.isAuthorOfPublication.latestForDiscovery | a01eb0e9-0843-4160-8075-9a85f0ee02fb |