2026-09-19T02:05:58Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1272972021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Michael J. Ricard and Juan Pablo Vielma.
Burnham, Katherine Lee.
Massachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
2020-09-15T21:50:48Z
2020-09-15T21:50:48Z
2020
2020
https://hdl.handle.net/1721.1/127297
1191901156
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
92 pages
eng
Massachusetts Institute of Technology
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
Operations Research Center.
Information fusion for an unmanned underwater vehicle through probabilistic prediction and optimal matching
Information fusion for an UUV through probabilistic prediction and optimal matching
Thesis
application/pdf
2020-09-15T21:50:47Z
Publication
Master
Sloan
OperRes
unknown
unknown
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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.</Abstract>
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