Information fusion for an unmanned underwater vehicle through probabilistic prediction and optimal matching
Author(s)
Burnham, Katherine Lee.
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.
Terms of use
Metadata
Show full item recordAbstract
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
2020Department
Massachusetts Institute of Technology. Operations Research Center; Sloan School of ManagementPublisher
Massachusetts Institute of Technology
Keywords
Operations Research Center.