Multisensor Modeling Underwater with Uncertain Information
This thesis develops an approach to the construction of multidimensional stochastic models for intelligent systems exploring an underwater environment. It describes methods for building models by a three- dimensional spatial decomposition of stochastic, multisensor feature vectors. New sensor...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/6980 |
| _version_ | 1826213893697961984 |
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| author | Stewart, W. Kenneth, Jr. |
| author_facet | Stewart, W. Kenneth, Jr. |
| author_sort | Stewart, W. Kenneth, Jr. |
| collection | MIT |
| description | This thesis develops an approach to the construction of multidimensional stochastic models for intelligent systems exploring an underwater environment. It describes methods for building models by a three- dimensional spatial decomposition of stochastic, multisensor feature vectors. New sensor information is incrementally incorporated into the model by stochastic backprojection. Error and ambiguity are explicitly accounted for by blurring a spatial projection of remote sensor data before incorporation. The stochastic models can be used to derive surface maps or other representations of the environment. The methods are demonstrated on data sets from multibeam bathymetric surveying, towed sidescan bathymetry, towed sidescan acoustic imagery, and high-resolution scanning sonar aboard a remotely operated vehicle. |
| first_indexed | 2024-09-23T15:56:31Z |
| id | mit-1721.1/6980 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:56:31Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/69802019-04-12T08:33:44Z Multisensor Modeling Underwater with Uncertain Information Stewart, W. Kenneth, Jr. This thesis develops an approach to the construction of multidimensional stochastic models for intelligent systems exploring an underwater environment. It describes methods for building models by a three- dimensional spatial decomposition of stochastic, multisensor feature vectors. New sensor information is incrementally incorporated into the model by stochastic backprojection. Error and ambiguity are explicitly accounted for by blurring a spatial projection of remote sensor data before incorporation. The stochastic models can be used to derive surface maps or other representations of the environment. The methods are demonstrated on data sets from multibeam bathymetric surveying, towed sidescan bathymetry, towed sidescan acoustic imagery, and high-resolution scanning sonar aboard a remotely operated vehicle. 2004-10-20T20:12:07Z 2004-10-20T20:12:07Z 1988-07-01 AITR-1143 http://hdl.handle.net/1721.1/6980 en_US AITR-1143 17839255 bytes 7028754 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Stewart, W. Kenneth, Jr. Multisensor Modeling Underwater with Uncertain Information |
| title | Multisensor Modeling Underwater with Uncertain Information |
| title_full | Multisensor Modeling Underwater with Uncertain Information |
| title_fullStr | Multisensor Modeling Underwater with Uncertain Information |
| title_full_unstemmed | Multisensor Modeling Underwater with Uncertain Information |
| title_short | Multisensor Modeling Underwater with Uncertain Information |
| title_sort | multisensor modeling underwater with uncertain information |
| url | http://hdl.handle.net/1721.1/6980 |
| work_keys_str_mv | AT stewartwkennethjr multisensormodelingunderwaterwithuncertaininformation |