Target Identification with Dynamic Hybrid Bayesian Networks

The continuous growth of data has created a demand for better data fusion algorithms. In this study we have used a method called Bayesian networks to answer the demand. The reason why Bayesian networks are used in wide range of applications is that modelling with Bayesian networks offers easy and straightforward representation for combining a priori knowledge with the observations. Another reason for growing use of the Bayesian networks is that Bayesian networks can combine attributes having different dimensions. In addition to the quite well-known theory of discrete and continuous Bayesian networks, we introduce a reasoning scheme to the hybrid Bayesian networks. The reasoning method used is based on polytree algorithm. Our aim is to show how to apply the hybrid Bayesian networks to identification. Also one method to achieve dynamic features is discussed. We have simulated dynamic hybrid Bayesian networks in order to identify aircraft in noisy environment.