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dc.contributor.advisorWhitaker, Lyn R.
dc.contributor.advisorNorton, Matthew
dc.contributor.authorLiraz, Shay Paz
dc.date.accessioned2018-10-26T19:21:55Z
dc.date.available2018-10-26T19:21:55Z
dc.date.issued2018-09
dc.identifier.urihttp://hdl.handle.net/10945/60431
dc.descriptionApproved for public release. distribution is unlimiteden_US
dc.description.abstractThe objective of this research is to develop a method for predicting the future behavior of ships and detecting anomalous behavior based on their past location coordinates and a set of context features. We use a Recurrent Neural Network model with inputs extracted from Automated Information System (AIS) data. This data includes ship coordinates, speed and course, and the ship's call sign, size, and type. These features are appropriately encoded to amplify significant predictive structures within the data. The ability to automate the task of track prediction and the process of detecting anomalous ship behavior serves to increase maritime domain awareness and aid security analysts in deciding how to best allocate limited resources. Furthermore, these capabilities enable the investigation of potential threats, prevention of collisions, and planning for search-and rescue missions.en_US
dc.description.urihttp://archive.org/details/shipstrajectorie1094560431
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsCopyright is reserved by the copyright owner.en_US
dc.titleSHIPS’ TRAJECTORIES PREDICTION USING RECURRENT NEURAL NETWORKS BASED ON AIS DATAen_US
dc.typeThesisen_US
dc.contributor.secondreaderKoyak, Robert A.
dc.contributor.departmentOperations Research (OR)
dc.subject.authorRecurrent Neural Networksen_US
dc.subject.authorAISen_US
dc.subject.authortrajectories predictionen_US
dc.subject.authormaritime assistanceen_US
dc.subject.authorRNNen_US
dc.subject.authorembeddingen_US
dc.subject.authorLSTMen_US
dc.subject.authorTensorFlowen_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceArmy, Israelen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.identifier.thesisid30155


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