  In real-world application scenarios, it is crucial for marine navigators and
security analysts to predict vessel movement trajectories at sea based on the
Automated Identification System (AIS) data in a given time span. This article
presents an unsupervised method of ship movement trajectory prediction which
represents the data in a three-dimensional space which consists of time
difference between points, the scaled error distance between the tested and its
predicted forward and backward locations, and the space-time angle. The
representation feature space reduces the search scope for the next point to a
collection of candidates which fit the local path prediction well, and
therefore improve the accuracy. Unlike most statistical learning or deep
learning methods, the proposed clustering-based trajectory reconstruction
method does not require computationally expensive model training. This makes
real-time reliable and accurate prediction feasible without using a training
set. Our results show that the most prediction trajectories accurately consist
of the true vessel paths.
