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This paper presents a Bayesian network-based approach to multisensor multitarget detection and tracking problem. The aim here is to propose an improvement of the probabilistic data association approach that takes into account contextual information about the scene. This information is modeled by a Bayesian network that allows a dynamic estimation of the detection probability of the PDA. Our approach is then applied to synthetic data from scanning radar that is mounted on a moving vehicle. The aim is to detect the surrounding objects and track them through the sequence.