  For decades, track association has been a challenging problem in marine
surveillance, which involves the identification and association of vessel
observations over time. However, the Automatic Identification System (AIS) has
provided a new opportunity for researchers to tackle this problem by offering a
large database of dynamic and geo-spatial information of marine vessels. With
the availability of such large databases, researchers can now develop
sophisticated models and algorithms that leverage the increased availability of
data to address the track association challenge effectively. Furthermore, with
the advent of deep learning, track association can now be approached as a
data-intensive problem. In this study, we propose a Long Short-Term Memory
(LSTM) based multi-model framework for track association. LSTM is a recurrent
neural network architecture that is capable of processing multivariate temporal
data collected over time in a sequential manner, enabling it to predict current
vessel locations from historical observations. Based on these predictions, a
geodesic distance based similarity metric is then utilized to associate the
unclassified observations to their true tracks (vessels). We evaluate the
performance of our approach using standard performance metrics, such as
precision, recall, and F1 score, which provide a comprehensive summary of the
accuracy of the proposed framework.

Machine Learning