{"id":{"repo_id":"nps","oai_identifier":"oai:calhoun.nps.edu:10945/60431"},"canonical_url":"https://search.dev.ndltd.org/etd/nps/oai:calhoun.nps.edu:10945/60431","repository":{"repo_id":"nps","name":"Naval Postgraduate School","base_url":"https://calhoun.nps.edu/server/oai/request"},"display":{"title":"SHIPS’ TRAJECTORIES PREDICTION USING RECURRENT NEURAL NETWORKS BASED ON AIS DATA","abstract":"The 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.","abstract_html":"The 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.","abstract_has_math":false,"creators":["Liraz, Shay Paz"],"institution":"Monterey, CA; Naval Postgraduate School","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Operations Research (OR)","school":null,"contributors":[],"advisors":["Whitaker, Lyn R.","Norton, Matthew"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09","date_published":"2018-09","updated_at":"2026-07-27T20:25:08Z","subjects":[],"languages":[],"rights":["Copyright is reserved by the copyright owner."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10945/60431","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Whitaker, Lyn R.","Norton, Matthew"]},{"key":"dc:contributor.department","label":"Department","values":["Operations Research (OR)"]},{"key":"dc:creator","label":"Author","values":["Liraz, Shay Paz"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-10-26T19:21:55Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-10-26T19:21:55Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-09"]},{"key":"dc:publisher","label":"Institution","values":["Monterey, CA; Naval Postgraduate School"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Copyright is reserved by the copyright owner."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10945/60431"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The 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."]},{"key":"dc:title","label":"Title","values":["SHIPS’ TRAJECTORIES PREDICTION USING RECURRENT NEURAL NETWORKS BASED ON AIS DATA"]}]}],"canonical_facts":{"dc:contributor.advisor":["Whitaker, Lyn R.","Norton, Matthew"],"dc:contributor.department":["Operations Research (OR)"],"dc:creator":["Liraz, Shay Paz"],"dc:date.accessioned":["2018-10-26T19:21:55Z"],"dc:date.available":["2018-10-26T19:21:55Z"],"dc:date.issued":["2018-09"],"dc:description.abstract":["The 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."],"dc:identifier.uri":["https://hdl.handle.net/10945/60431"],"dc:publisher":["Monterey, CA; Naval Postgraduate School"],"dc:rights":["Copyright is reserved by the copyright owner."],"dc:title":["SHIPS’ TRAJECTORIES PREDICTION USING RECURRENT NEURAL NETWORKS BASED ON AIS DATA"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:25:08Z"}