Open Access Open Access  Restricted Access Subscription Access

Hybrid Bayesian Blackboard and Structure-variable Dynamic Bayesian Networks Approach to UCAV Situation Assessment

Qinan Luo, Haibin Duan, Xiaoguang Hu, Guofeng Zhang, Yunpeng Zhang

Abstract


Unmanned Combat Aerial Vehicle (UCAV) is an inevitable trend of the future intelligent and unmanned flight platform. Situation assessment is an effective method to solve the battlefield autonomous decision-making problem. The process of situation assessment and the concept and inference of static Bayesian networks are introduced. Then a Bayesian blackboard method with knowledge sources modify Bayesian networks is analyzed. Structure-variable dynamic Bayesian networks is put forward to deal with situation changes. Finally, the hybrid Bayesian blackboard and structure-variable dynamic Bayesian network is proposed for the highly dynamic air combat situation assessment and the model is proposed. The simulation results show that the inference results are preferable and consistent with the analysis.

Full Text:

PDF

References


H. B. Duan, X. X. Wei, and Z. N. Dong, “Multiple UCAVs cooperative air combat simulation platform based on PSO, ACO, and game theory,” IEEE Aerospace and Electronic Systems Magazine, vol. 28, no. 1, pp. 12-19, 2013.

W. Yi, S. Y, L. J. Y, and X. S. Tao, “Air defense threat assessment based on dynamic Bayesian network.” Proceedings of the IEEE 2012 International Conference on Systems and Informatics (ICSAI), pp. 721-724, 2012.

S. Sumit, D. Pedro, and W. Daniel, “Relational dynamic bayesian networks,” Journal of Artificial Intelligence Research, vol. 24, pp. 759-797, 2005.

L. B. Johnson, S. S. Ponda, H. L. Choi, and J. P. How, “Asynchronous decentralized task allocation for dynamic environments,” Proceedings of the AIAA Infotech and Aerospace Conference, St. Louis, MO, March 2011.

C. Sutton, C. Morrison, P. R. Cohen, J. Moody, and J. Adibi, “A bayesian blackboard for information fusion,” Proceedings of the Seventh International Conference on Information Fusion, vol. 2, Mountain View, USA, 2004.

H. Cheng, J. Page, and J. Olsen, “Cooperative control of UAV swarm via information measures,” International Journal of Intelligent Unmanned Systems, vol. 1, no. 3, pp. 256-275, 2013.

J. Pearl, “Fusion, propagation, and structuring in belief networks,” Artificial Intelligence, vol. 29, no. 3, pp. 241-288, 1986.

S. M. Mahoney, and K. B. Laskey, “Constructing situation specific belief networks,” Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., pp. 370-378, 1998.

K. B. Laskey, and S. M. Mahoney, “Network fragments for knowledge-based construction of belief networks.” Proceedings of the AAAI Symposium on Mixed-Initiative Reasoning, 1998.

K. B. Laskey, and S. M. Mahoney, “Network fragments: representing knowledge for constructing probabilistic models,” Proceedings of Thirteenth Annual Conference on uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., pp. 334-341, 1997.

S. M. Mahoney, and K. B. Laskey, “Network engineering for complex belief networks,” Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96). San Francisco, pp. 389-396, 1996.

S. Sharma, “A kushner approach for small random perturbations of the duffing-van der pol system,” Automatica, vol. 45, no. 4, pp. 1097-1099,

H. Tu, J. Allanach, S. Singh, K. R. Pattipati, and P. Willett, “Information integration via hierarchical and hybrid bayesian networks,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 36, no. 1, pp. 19-33, 2006.

D. Lee, “Nonlinear estimation and multiple sensor fusion using unscented information filtering,” IEEE Signal Processing Letters, vol. 15, pp. 861-864, 2008.

C. Rasmussen, and C. Williams, Gaussian Processes for Machine Learning, MIT Press, Cambridge, MA, 2006.

T. Campbell, S. Ponda, G. Chowdhary, and J. P. How, “Planning under

uncertainty using bayesian nonparametric models,” Proceedings of the AIAA Guidance, Navigation, and Control Conference, Minneapolis, Minnesota, August 2012.

V. Lee, J. Yoon, and P. Vedula, “Nonlinear estimation and bayesian multi-sensor fusion using adaptive quadrature” Proceedings of the AIAA Guidance, Navigation, and Control Conference, Portland, Oregon, August 2011.




DOI: http://dx.doi.org/10.21535%2FProICIUS.2014.v10.279

Refbacks

  • There are currently no refbacks.