Active information fusion for decision making under uncertainty

Many information fusion applications especially in military domains are often characterized as a high degree of complexity due to three challenges: 1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; 2) decision must be made quickly; and 3) the world situation as well as sensory observations evolve over time. In this paper, we propose a dynamic active information fusion framework that can simultaneously address the three challenges. The proposed framework is based on Dynamic Bayesian Networks (DBNs) with an embedded active sensor controller. The DBNs provide a coherent and unified hierarchical probabilistic framework to represent, integrate and infer corrupted dynamic sensory information of different modalities. The sensor controller allows it to actively select and invoke a subset of sensors to produce the sensory information that is most relevant to the current task with reasonable time and limited resources. The proposed framework can therefore provide dynamic, purposive and sufficing information fusion particularly well suited to applications where the decision must be made from dynamically available information of diverse and disparate sources. To verify the proposed framework, we use target recognition problem as a proof-of-concept. The experimental results demonstrate the utility of the proposed framework in efficiently modeling and inferring dynamic events.