Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 89-92). / 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. / by Katherine Lee Burnham. / S.M. / S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/127297 |
Date | January 2020 |
Creators | Burnham, Katherine Lee. |
Contributors | Michael J. Ricard and Juan Pablo Vielma., Massachusetts Institute of Technology. Operations Research Center., Massachusetts Institute of Technology. Operations Research Center |
Publisher | Massachusetts Institute of Technology |
Source Sets | M.I.T. Theses and Dissertation |
Language | English |
Detected Language | English |
Type | Thesis |
Format | 92 pages, application/pdf |
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., http://dspace.mit.edu/handle/1721.1/7582 |
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