SHIPS’ TRAJECTORIES PREDICTION USING RECURRENT NEURAL NETWORKS BASED ON AIS DATA

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.

Department dc:contributor.department

Operations Research (OR)

Grantor dc:publisher

Monterey, CA; Naval Postgraduate School

Year dc:date.issued

2018

Author dc:creator

• Liraz, Shay Paz

Advisors dc:contributor.advisor

• Whitaker, Lyn R.
• Norton, Matthew

Statement dc:rights

• Copyright is reserved by the copyright owner.

Handle dc:identifier.uri

https://hdl.handle.net/10945/60431

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/60431