Machine Learning for Enhanced Maritime Situation Awareness: Leveraging Historical AIS Data for Ship Trajectory Prediction
Paper I: Murray, B. & Perera, L.P. (2021). Ship Behavior Prediction via Trajectory Extraction-Based Clustering for Maritime Situation Awareness. (Accepted manuscript). In press in Journal of Ocean Engineering and Science. Also available in Munin at https://hdl.handle.net/10037/20914.
Paper II: Murray, B. & Perera, L.P. (2020). A Dual Linear Autoencoder Approach for Vessel Trajectory Prediction Using Historical AIS Data. Ocean Engineering, 209, 107478. Also available in Munin at https://hdl.handle.net/10037/18366.
Paper III: Murray, B. & Perera, L.P. (2020). Unsupervised Trajectory Anomaly Detection for Situation Awareness in Maritime Navigation. Proceedings of the 39th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2020). ASME. Also available at https://doi.org/10.1115/OMAE2020-18281.
Paper IV: Murray, B. & Perera, L.P. (2021). Deep Representation Learning-Based Vessel Trajectory Clustering for Situation Awareness in Ship Navigation. Accepted for Publication in Developments in Maritime Technology and Engineering. Proceedings of the 5th International Conference on Maritime Technology and Engineering (MARTECH 2020). Taylor and Francis, forthcoming.
Paper V: Murray, B. & Perera, L.P. An AIS-Based Deep Learning Framework for Regional Ship Behavior Prediction. (Submitted manuscript).
PublisherUiT Norges arktiske universitet
UiT The Arctic University of Norway
The following license file are associated with this item: