{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/127297"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/127297","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Information fusion for an unmanned underwater vehicle through probabilistic prediction and optimal matching","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. 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