Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks.
Many information fusion applications are often characterized by a high degree of complexity because: (1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; (2) decisions must be made efficiently; and (3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.
Zhang
Yongmian
Y
Ji
Qiang
Q
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003661:Decision Support Techniques; D016247:Information Storage and Retrieval / Q000379:methods*; D016015:Logistic Models; D015233:Models, Statistical; D016571:Neural Networks, Computer; D014159:Transducers