Affiliation:
1. Aviation University of Air Force
Abstract
This paper presents a selective incremental information fusion method based on Bayesian network, so that the fusion algorithm can actively select the most relevant information and decision-making, and can make the fusion model to adapt to the dynamic changes in the external environment, and sensor information selection, fusion, decision-making integrated in the framework of Bayesian network . The experimental results show that this method is better than the traditional method.
Publisher
Trans Tech Publications, Ltd.
Reference7 articles.
1. YM. Zhang, Q. Ji. Active and dynamic information fusion for multisensor systems withdynamic Bayesian networks [J]. IEEE Transactions on Systems Man and Cybernetics-Part B: Cybernetics. 2006, 36(2): 467-472.
2. H. Wang, K. Yao, G. Pottie, and D. Estrin. Entropy-based sensor selection heuristic for target localization [C]. 3rd International Symposium on Information Processing in Sensor Networks. 2004, 36-45.
3. YM. Zhang, Q. Ji. Efficient Sensor Selection for Active Information Fusion [J]. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics. 2010, 40(3): 719-728.
4. WH. Liao, Q. Ji. Efficient Active Fusion for Decision-making via VOI Approximation[C]. 21st AAAI Conference on Artificial Intelligence (AAAI-2006). 2006, 1180-1185.
5. C. Kreucher, K. Kastella, and A. O. Hero. Sensor management using an active sensing approach [J]. Signal Process. 2005, 85(3): 607-624.
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2 articles.
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