Abstract:
In this study, a method is developed for modelling and classifying behaviour of maritime vessels in
a piracy situation. Prior knowledge is used to construct a probabilistic graphical model of maritime
vessel behaviour. This model is a novel variant of a dynamic Bayesian network (DBN), that extends
the switching linear dynamic system (SLDS) to accommodate contextual information. A generative
model and a classifier model are developed. The purpose of the generative model is to generate
simulated data by modelling the behaviour of fishing vessels, transport vessels and pirate vessels in a
maritime piracy situation. The vessels move, interact and perform various activities on a predefined
map. A novel methodology for evaluating and optimising the generative model is proposed. This
methodology can easily be adapted to other applications. The model is evaluated by comparing
simulation results with 2011 pirate attack reports. The classifier model classifies maritime vessels
into predefined categories according to their behaviour. The classification is performed by inferring
the class of a vessel as a fishing, transport or pirate vessel class. The classification method is evaluated
by classifying the data generated by the generative model and comparing it to the true classes of the
simulated vessels.
