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Title: Unsupervised vessel trajectory reconstruction
A trajectory is a sequence of observations in time and space, for examples, the path formed by maritime vessels, orbital debris, or aircraft. It is important to track and reconstruct vessel trajectories using the Automated Identification System (AIS) data in real-world applications for maritime navigation safety. In this project, we use the National Science Foundation (NSF)'s Algorithms for Threat Detection program (ATD) 2019 Challenge AIS data to develop novel trajectory reconstruction method. Given a sequence ofNunlabeled timestamped observations, the goal is to track trajectories by clustering the AIS points with predicted positions using the information from the true trajectoriesΧ. It is a natural way to connect the observed pointxîwith the closest point that is estimated by using the location, time, speed, and angle information from a set of the points under considerationxi∀i∈ {1, 2, …,N}. The introduced method is an unsupervised clustering-based method that does not train a supervised model which may incur a significant computational cost, so it leads to a real-time, reliable, and accurate trajectory reconstruction method. Our experimental results show that the proposed method successfully clusters vessel trajectories.  more » « less
Award ID(s):
1924792
PAR ID:
10479167
Author(s) / Creator(s):
;
Publisher / Repository:
Frontiers
Date Published:
Journal Name:
Frontiers in Applied Mathematics and Statistics
Volume:
9
ISSN:
2297-4687
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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