International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 3, No. 5, 8 Oct. 2011
Cover page and Table of Contents: PDF (size: 306KB)
Robustness Evaluation for Military Communication Effectiveness based on Multiple Data Sources and Monte Carlo Simulation
Full Text (PDF, 306KB), PP.1-9
Views: 0 Downloads: 0
Index Terms
Military communication effectiveness, ro-bustness evaluation, data fusion, Monte Carlo simulation, Probability of Best
Abstract
In the choice process of optimal military commu-nication (MC) alternative, evaluation data mainly come from expert judgments, simulation results and test bed data, and they cannot be directly used in evaluation because of differences in form and attribute; and the MC environment changes rapidly as the operation tempo increasing. It is an important effort to judge the effectiveness robustness of MC alternative, since both the evaluation data and the MC envi-ronment are full of uncertainty. A robustness evaluation method based on multiple data sources and Monte Carlo simluation is proposed with respect to the characteristics of them. Mainly include Belief map as data expression form; Regression relational model built with Support Vector Re-gression (SVR) to acquire simulation data’s confidence with test bed data as training example; Extensive Bayesian Algo-rithm (EBA) to fuse data from multiple sources; Beta distri-bution fitting method for each criterion of each alternative by using the fused results; and calculation of the Probability of Best (PoB) of each alternative through Monte Carlo simu-lation. Take MCE evaluation of a Naval Vessels Fleet as an example, the proposed method is compared with some gen-eral methods. The results indicate that the proposed method helps to obtain relatively conservative alternative and is effective in guaranteeing the robustness.
Cite This Paper
Fuli Shi, Chao Li, Yifan Zhu, "Robustness Evaluation for Military Communication Effectiveness based on Multiple Data Sources and Monte Carlo Simulation", International Journal of Modern Education and Computer Science(IJMECS), vol.3, no.5, pp.1-9, 2011. DOI:10.5815/ijmecs.2011.05.01
Reference
[1]K.Su, J.Zhang, and Q.Yu, “Research on Military Intelli-gence Decision Support System based on Object-oriented Simulation,” Proceedings of 2007 IEEE International Con-ference on Grey Systems and Intelligent Services, Nanjing, China, November 18-20, 2007, pp.1246-1249.
[2]F.L. Shi, F. Yang, Q. Li, and Y.F. Zhu, “Simulation Based Effectiveness Evaluation of Military Communication Net-work with SEA Evaluation Operator”. Fire control and command control, Beijing, China, 2011, in press.
[3]Y.Y. Huang, “Research on the Robustness Evaluation Me-thod of Operational Effectiveness of Weapon Equipments and Its Supporting Techniques,” PhD Dissertation, National University of Defense Technology, Changsha, Hunan, China, 2006.
[4]Robust Decisions Inc, “ACCORD ™ Users Manual Ver-sion 2.5,” Corvallis OR. 2008.
[5]N.Y. Deng and Y.J. Tian, “Support Vector Machines——TheoryćArithmetic and Prolongationl”, Beijing: Science Press, China, 2009, pp.101-111.
[6]F. Markowetz, “Support Vector Machines in Bioinformatics. Diploma Thesis in Mathematics,” University of Heidelberg, Germany.2001.
[7]O.J. Berger, “Statistical Decision Theory and Bayesian Analysis Springer Verlag”, New York: Inc, 1985.
[8]D.A. Bruce, “Bayesian Methods for Collaborative Decision-making,” Robust Decisions Inc, 2003, pp.1-6.
[9]W.Wang, H.Y.Zhou, and G.J.Yin. “Bayes Method with Mixed Beta Distribution”. System Engineering Theory & Practice, Beijing, China, 2005, vol.9, pp.142-144.
[10]M.Creutz, “Micro Ccanonical Monte Carlo Simulation”. Physical Review Letters, 1983, pp. 1411-1414.
[11]J.P. Li, “Study on Fitting Methods of Simulation Metamo-delling and Its Application,” PhD Dissertation, National University of Defense Technology, Changsha, Hunan, China, 2007.
[12]S.Gao and S.C. Lou, “Progress in Assessing the Effective-ness of Weapon System”. System engineering theory & practice, 1998, vol.7, pp.57-62.
[13]Communications Research Centre. “Improving Scalability of Heterogeneous Wireless Networks with Hierarchical OLSR”. Defence R&D Canada. August 2004.
[14]H.Tang and J.Zhang, “A Framework of Intelligent Decision Support System of Military Communication Network Effectiveness Evaluation,” Proceedings 5th International Conference on Fuzzy Systems and Knowledge Discovery, vol4, 2008, pp. 518-521.
[15]L. Wang, W.P. Wang, and F. Yang, “OASIS: A Universal Environment for Weapon Equipment System of Systems Evaluation,” Forum of Advanced Defense Science and Technology, Changsha, Hunan, China, 2007.
[16]H.Z. Kang, C. Butler, and Q.P. Yang, “A New Survivabili-ty Measure for Military Communication Networks,” Pro-ceedings of IEEE Milcom, 1998, pp. 71-75.