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

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Authors
Liraz, Shay Paz
Subjects
Recurrent Neural Networks
AIS
trajectories prediction
maritime assistance
RNN
embedding
LSTM
TensorFlow
Advisors
Whitaker, Lyn R.
Norton, Matthew
Date of Issue
2018-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
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.
Type
Thesis
Description
Department
Operations Research (OR)
Other Units
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Distribution Statement
Approved for public release; distribution is unlimited.
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Copyright is reserved by the copyright owner.
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