Multi-Level Information Fusion Approach with Dynamic Bayesian Networks for an Active Perception of the environment

Most Situation Awareness applications in Information Fusion try to evaluate a dynamic environment by a passive approach combining heterogeneous information. However in a crisis situation, the decision has to be made efficiently as the world quickly evolves. A consequence is the difficulty to get the information in a fast and efficient way with an acceptable confidence. Another problem is the processing of a significant amount of heterogeneous information in near real time. To address this issue, we propose a multi-level Information Fusion framework based on Dynamic Bayesian Networks (DBN) with an active perception approach. The contribution of this model primarily lies in its capability of handling both Hard & Soft sensors and resulting information. And secondly in the identification of the most valuable DBN variables which maximize the information gains in the next step. These valuable variables allow to infer states on a sub-DBN to reduce the complexity of calculation. In this top-down approach, we seek which variables can provide the most valuable information to select automatically the right sensors and choose correct actions to optimally observe these variables. We finally propose an illustration with a basic maritime scenario.