Author ORCID Identifier
Semester
Fall
Date of Graduation
2023
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Industrial and Managements Systems Engineering
Committee Chair
Imtiaz Ahmed
Committee Member
Srinjoy Das
Committee Member
Bin Liu
Abstract
In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the Automatic Identification System, which serves as a communication medium between neighboring ships and the control center. While traditional methods have relied on sequential tracking and physics-based models, the emergence of deep learning has significantly transformed techniques typically used in trajectory prediction, clustering, and anomaly detection. This transformation is largely attributed to the deep learning algorithm’s capability to model complex nonlinear relationships while capturing both the spatial and temporal dynamics of ship movement. Capitalizing on this computational advantage, our study focuses on evaluating different deep learning architectures such as Multi Model Long Short-Term Memory (LSTM), 1D Convolutional-LSTM, and Temporal-Graph Convolutional Neural Networks— in addressing the problem of track association. The performance of these proposed models are compared against different deep learning algorithms specialized in track association tasks using several real-life AIS datasets.
Recommended Citation
Syed, Md Asif Bin, "Spatio-Temporal Deep Learning Approaches for Addressing Track Association Problem using Automatic Identification System (AIS) Data" (2023). Graduate Theses, Dissertations, and Problem Reports. 12227.
https://researchrepository.wvu.edu/etd/12227