Information fusion for an unmanned underwater vehicle through probabilistic prediction and optimal matching
Name
1191901156-MIT.pdf
Size
3.67 MB
Format
Adobe PDF
Checksum (MD5)
281f8cd72dbb09904a12af56527913ad
Author(s)
Burnham, Katherine Lee.
Advisor(s)
Michael J. Ricard and Juan Pablo Vielma.
Alternative Title
Information fusion for an UUV through probabilistic prediction and optimal matching
Date Issued
2020
Publisher
Massachusetts Institute of Technology
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).
Subjects
Operations Research Center.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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