# Target Recognition and Behavior Prediction based on Bayesian Network
##### Volume 15, Number 3, March 2019, pp. 1014-1022 DOI: 10.23940/ijpe.19.03.p31.10141022
## Chao Lin^{a} and Yanan Liu^{b}
^{a}The 44 unit of PLA 92941 Army, Huludao, China^{b}China Research Institute of Radiowave Propagation, Qingdao, China
(Submitted on November 5, 2018; Revised on December 6, 2018; Accepted on January 3, 2019)
## Abstract:
The identification of target identity attributes and its behavioral prediction are important means for providing command and decision support in modern warfare. This paper analyzes the key steps in the process of target recognition and behavior prediction and proposes a target recognition and behavior prediction model based on the Bayesian network. In the simulation example, by integrating the sensor information and combining expert knowledge, the model can effectively and accurately conduct battlefield situational awareness. Combined with the background of big data, this paper introduces the distributed processing system Hadoop, and prospects its application in target recognition and behavior prediction.
**References: 32**
- M. R. Endsley, “Toward a Theory of Situation Awareness in Dynamic Systems,” Human Factors, Vol. 37, No. 1, pp. 32-64, 2016
- J. Pearl, “Fusion, Propagation, and Structuring in Belief Networks,” Elsevier Science Publishers Ltd., 1986
- S. L. Lauritzen and D. J. Spiegelhalter, “Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems,” Journal of the Royal Statistical Society, Vol. 50, No. 2, pp. 157-224, 1988
- Hugin Expert, “The Leading Decision Support Tool,” (https://www.hugin.com)
- J. Liu, “Bayesian Network Inference on Risks of Construction Schedule-Cost,” in Proceedings of International Conference of Information Science and Management Engineering, pp. 15-18, IEEE Computer Society, 2010
- Z. Shi, Y. Guo, and Y. Li, “Intelligent Decision-Making Modeling for Unmanned Aerial Vehicle based on Probability Graphical Models,” in Proceedings of IEEE Fifth International Conference on Advanced Computational Intelligence, pp. 304-308, IEEE, 2012
- Z. J. Ma, Q. Shi, and B. Li, “Battle Damage Assessment based on Bayesian Network [C],” in Proceedings of Eighth Acis International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, IEEE, pp. 388-391, 2007
- G. Laskey, W. Navy, Y. Dc, et al., “Combat Identification with Bayesian Networks,” Combat Identification with Bayesian Networks, 2002
- D. W. Franzen, “A Bayesian Decision Model for Battle Damage Assessment,” Master’s Thesis, Helsinki University of Technology, 1999
- J. C. Ramirez, G. Munoz, and L. Gutierrez, “Fault Diagnosis in an Industrial Process using Bayesian Networks: Application of the Junction Tree Algorithm,” in Proceedings of Electronics, Robotics and Automotive Mechanics Conference, pp. 301-306, IEEE Computer Society, 2009
- G. F. Cooper, “The Computational Complexity of Probabilistic Inference using Bayesian Belief Networks (Research Note),” Elsevier Science Publishers Ltd., 1990
- P. P. Shenoy and G. Shafer, “Axioms for Probability and Belief-Function Propagation,” Readings in Uncertain Reasoning, Morgan Kaufmann Publishers Inc., pp. 169-198, 1990
- N. L. Zhang and D. Poole, “A Simple Approach to Bayesian Network Computations,” 1994
- M. C. Golumbic, “Algorithmic Graph Theory and Perfect Graphs,” Academic Press, pp. 207-208, 1980
- S. L. Lauritzen, “The EM Algorithm for Graphical Association Models with Missing Data,” Computational Statistics & Data Analysis, Vol. 19, No. 2, pp. 191-201, 1995
- J. Cheng, D. A. Bell, and W. Liu, “Learning Belief Networks from Data: An information Theory based Approach,” Artificial Intelligence, Vol. 137, No. 1-2, pp. 325-331, 1997
- G. F. Cooper and E. Herskovits, “A Bayesian Method for the Induction of Probabilistic Networks from Data,” Machine Learning, Vol. 9, No. 4, pp. 309-347, 1992
- M. R. Endsley, “Toward a Theory of Situation Awareness in Dynamic Systems,” Human Factors, Vol. 37, No. 1, pp. 32-64, 2016
- A. B. Mrad, V. Delcroix, S. Piechowiak, et al., “Understanding Soft Evidence as Probabilistic Evidence: Illustration with Several Use Cases,” in Proceedings of International Conference on Modeling, Simulation and Applied Optimization, pp. 1-6, IEEE, 2013
- R. Pan, Y. Peng, and Z. Ding, “Belief Update in Bayesian Networks using Uncertain Evidence,” in Proceedings of IEEE International Conference on Tools with Artificial Intelligence, pp. 441-444, IEEE, 2006
- A. N. Steinberg, C. L. Bowman, and F. E. White, “Revisions to the JDL Data Fusion Model,” in Proceedings of SPIE - The International Society for Optical Engineering, Vol. 3719, pp. 430-441, 1999
- T. M. Schuck, J. B. Hunter, and D. D. Wilson, “Multi-Hypothesis Structures and Taxonomies for Combat Identification Fusion,” in Proceedings of 2004 Aerospace Conference, Vol. 3, pp. 2026, IEEE, 2004
- X. Q. Cheng, X. L. Jin, Y. Z. Wang, et al., “A Literature Review on Big Data System and Analysis Technology,” Journal of Software, Vol. 9, pp. 1889-1908, 2014
- S. O. Fadiya, S. Saydam, and V. V. Zira, “Advancing Big Data for Humanitarian Needs,” Procedia Engineering, Vol. 78, pp. 88-95, 2014
- Fazal-e-Amin, A. S. Alghamdi, I. Ahmad, and T. Hussain, “Big Data for C4i Systems: Goals, Applications, Challenges and Tools,” in Proceedings of Fifth International Conference on the Innovative Computing Technology (INTECH 2015), pp. 89-93, Galcia, 2015
- S. Ghemawat, H. Gobioff, and S. T. Leung, “The Google File System,” Acm Sigops Operating Systems Review, Vol. 37, No. 5, pp. 29-43, 2003
- J. Dean and S. Ghemawat, “Map Reduce: Simplified Data Processing on Large Clusters,” ACM, 2008
- T. White, “Hadoop - The Definitive Guide 4e,” Hadoop: The Definitive Guide, O’Reilly Media, Inc., pp. 1-4, 2015
- N. Ma, Y. Xia, and V. K. Prasanna, “Parallel Exact Inference on Multicore using MapReduce,” in Proceedings of the 2012 IEEE 24th International Symposium on Computer Architecture and High Performance Computing, pp. 187-194, 2012
- Y. Zhao, J. Xu, and Y. Gao, “A Parallel Algorithm for Bayesian Network Parameter Learning based on Factor Graph,” in Proceedings of IEEE International Conference on Tools with Artificial Intelligence, pp. 506-511, IEEE Computer Society, 2013
- N. Jongsawat and W. Premchaiswadi, “Solving the NP-Hard Computational Problem in Bayesian Networks using Apache Hadoop MapReduce,” in Proceedings of International Conference on ICT and Knowledge Engineering, pp. 1-5, IEEE, 2013
- A. Basak, I. Brinster, X. Ma, et al., “Accelerating Bayesian Network Parameter Learning using Hadoop and MapReduce,” in Proceedings of International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, ACM, pp. 101-108, 2012
Please note : You will need Adobe Acrobat viewer to view the full articles.
^{a}*The 44 unit of PLA 92941 Army*,* Huludao*,* China*
^{b}China Research Institute of Radiowave Propagation,* Qingdao*, *China* |