A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and
classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN).
Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating
various contextual elements that influence behaviour. The entity is classified according to its behaviour.
Classification is expressed as a conditional probability of the entity class given its tracked trajectory and
the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The
model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel
aspects of this work include the unified approach to behaviour modelling and classification, the way in
which contextual information is fused, the unique approach to classification according to behaviour
and the associated derived Gaussian sum filter inference algorithm.