dc.contributor.advisor | Michael J. Ricard and Juan Pablo Vielma. | en_US |
dc.contributor.author | Burnham, Katherine Lee. | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Operations Research Center. | en_US |
dc.date.accessioned | 2020-09-15T21:50:48Z | |
dc.date.available | 2020-09-15T21:50:48Z | |
dc.date.copyright | 2020 | en_US |
dc.date.issued | 2020 | en_US |
dc.identifier.uri | https://hdl.handle.net/1721.1/127297 | |
dc.description | Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020 | en_US |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (pages 89-92). | en_US |
dc.description.abstract | This thesis presents a method for information fusion for an unmanned underwater vehicle (UUV).We consider a system that fuses contact reports from automated information system (AIS) data and active and passive sonar sensors. A linear assignment problem with learned assignment costs is solved to fuse sonar and AIS data. Since the sensors operate effectively at different depths, there is a time lag between AIS and sonar data collection. A recurrent neural network predicts a contact's future occupancy grid from a segment of its AIS track. Assignment costs are formed by comparing a sonar position with the predicted occupancy grids of relevant vessels. The assignment problem is solved to determine which sonar reports to match with existing AIS contacts. | en_US |
dc.description.statementofresponsibility | by Katherine Lee Burnham. | en_US |
dc.format.extent | 92 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Operations Research Center. | en_US |
dc.title | Information fusion for an unmanned underwater vehicle through probabilistic prediction and optimal matching | en_US |
dc.title.alternative | Information fusion for an UUV through probabilistic prediction and optimal matching | en_US |
dc.type | Thesis | en_US |
dc.description.degree | S.M. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Operations Research Center | en_US |
dc.contributor.department | Sloan School of Management | |
dc.identifier.oclc | 1191901156 | en_US |
dc.description.collection | S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center | en_US |
dspace.imported | 2020-09-15T21:50:47Z | en_US |
mit.thesis.degree | Master | en_US |
mit.thesis.department | Sloan | en_US |
mit.thesis.department | OperRes | en_US |