Abstract
Facing asymmetric threats in a network centric environment, modern naval command and control systems confront increasingly demanding challenges in data fusion. It is very important to efficiently and promptly predict the enemy’s or adversary tactical intention from level 2 data fusion. In this paper, a layered intention model is proposed to represent the uncertain elements relating to adversarial intention and their uncertain relations in naval battlefield domain. The main ideal of this paper is to develop a hierarchical Bayesian network based on situation-specific Bayesian network (SSBN) and dynamic Bayesian network (DBN) that can be adapted to cope with the multi-timescales layered intention recognition problem.
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