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Information fusion for an unmanned underwater vehicle through probabilistic prediction and optimal matching

Author(s)
Burnham, Katherine Lee.
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Download1191901156-MIT.pdf (3.672Mb)
Alternative title
Information fusion for an UUV through probabilistic prediction and optimal matching
Other Contributors
Massachusetts Institute of Technology. Operations Research Center.
Advisor
Michael J. Ricard and Juan Pablo Vielma.
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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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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.
Description
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).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/127297
Department
Massachusetts Institute of Technology. Operations Research Center; Sloan School of Management
Publisher
Massachusetts Institute of Technology
Keywords
Operations Research Center.

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