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Target identification with Bayesian networks

Tracking algorithms can tell fairly reliable where target is heading. That is enough in civilian aviation, but in defence applications it might be not. Target's type and its hostility are at least as important. Normally, identification of type or friend or foe cannot be determined from target's kinematic information. To identify a target we also need other information. Every plane type has its own specialities e.g. we know that certain type has two engines which affects directly to heat of exhaust fumes. This kind of speciality is generally referred as an attribute information. Because attribute information is type depended, it must be modelled by an expert, who has beforehand knowledge of the target's causality relations. One of the best theories to get expert's knowledge into a tracking system is Bayesian networks. Bayesian networks is a model that describes relationships between attributes. In this paper we concentrate to identification problem. Question is how comprehension of the target's type changes with time when observations are corrupted by noise. We illustrate theory of Bayesian networks and explain its place in racking system. Finally we analyze performance of Bayesian networks in case where the problem is to identify targets from noisy data set.