Information fusion for an unmanned underwater vehicle through probabilistic prediction and optimal matching
Author(s)Burnham, Katherine Lee.
Information fusion for an UUV through probabilistic prediction and optimal matching
Massachusetts Institute of Technology. Operations Research Center.
Michael J. Ricard and Juan Pablo Vielma.
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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.
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020Cataloged from PDF version of thesis.Includes bibliographical references (pages 89-92).
DepartmentMassachusetts Institute of Technology. Operations Research Center; Sloan School of Management
Massachusetts Institute of Technology
Operations Research Center.