The Presentation of a Model for Analyzing the Behavior of the Enemy Using Hidden Markov Models Based on Electronic Warfare Observations in Complex War Scenes

Document Type : Original Article

Authors

1 imam hossein university

2 modarres tarbiat university

Abstract

Modeling is one of the basic tools for planning complex wars. Today’s wars are very different due to the complexity and dynamism of the scenes compared with traditional wars and require the rapid and dynamic command and control so that they can react quickly against the changes in the battle scene and make     decisions. In the information age, with the complexity of the battle scenes and the digitization of the        battlefield, the observations of commanders are made using electronic warfare systems. In this paper, the sensemaking process of the stimuli and physical actions of the enemy in the war scene, which expresses our intuitive appreciation of the situation, has been modeled using hidden Markov models (HMM) based on the electronic warfare observations. This model has been used to analyze the behavior of the enemy and       determine his operational objectives for the military decision-making process in order to adopt an          appropriate response to the enemy. For this purpose, a possible United States’ war scenario against the Islamic Republic of Iran has been studied from an electronic warfare perspective and used as the base of modeling. The time-invariant hidden Markov model of the first-order type has been considered, implying that all probabilities that describe the model do not change with time. The results of simulations show that this model is a good way to determine the enemy’s operational objectives and the decision-making process based on the electronic warfare observations of physical actions in complex war scenes.

Keywords

References

[1] S. H. Mohammadi Najm, “Cognitive war, the fifth dimension of the war,” 1st ed., Tehran: Center for Future Studies of Defense Science and Technology, Institute for Defense Research and Training, 2009. (In Persian) ##
[2] E. A. Smith, “Complexity, Networking, and Effects-Based Operations: Approaching the" how to" of EBO,” DTIC Document, 2005.##
[3] E. A. Smith, “Effects based operations: Applying network centric warfare in peace, crisis, and war,” DTIC Document, 2006.##
[4] M. Frater and M. Ryan, “Electronic warfare for the digitized battlefield,” Artech House, Inc., Norwood, MA, USA, 2001.##
[5] R. Wolfe and M. J. Abramson, “Modern statistical methods in respiratory medicine,” Respirology, vol. 19, no. 1, pp. 9-13, 2014.##
[6] M. A. Pimentel, M. D. Santos, D. B. Springer, and G. D. Clifford, “Heart beat detection in multimodal physiological data using a hidden semi-Markov model and signal quality indices,” Physiological Measurement, vol. 36, no. 8, p. 1717, 2015.##
[7] C. Zhou, S. Huang, N. Xiong, SH. Yang, H. Li, Y. Qin, et al., “Design and Analysis of Multimodel-Based Anomaly Intrusion Detection Systems in Industrial Process Automation,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 45, no. 10, pp. 1345-1360, 2015.##
[8] M. H.  Moattar, “Hidden Markov Model and Training Algorithms,” Dept. of Computer Engineering and Information Technology, AmirKabir University of Technology, Tehran, 2006. (In Persian)##
[9] R. Marxer and H. Purwins, “Unsupervised Incremental Online Learning and Prediction of Musical Audio Signals,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 5, pp. 863-874, 2016.##
[10] B. B. Vizzotto, B. Zatt, M. Shafique, S. Bampi, and et al., “Model Predictive Hierarchical Rate Control With Markov Decision Process for Multiview Video Coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23 , no. 12, pp. 2090-2104, 2013.##
[11] K. Vimala, “Stress Causing Arrhythmia Detection from ECG Signal using HMM,” IJIRCCE, vol. 2, no. 10, pp.        6079-6085, 2014.##
[12] S. Nootyaskool and W. Choengtong, “Hidden Markov Models predict foreign exchange rate,” Communications and Information Technologies (ISCIT), 14th International Symposium on Incheon, pp. 99-101, 2014.##
[13] X. Chen, H. Zhang, AB. MacKenzie, and M. Matinmikko, “Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model,” IEEE Wireless Communications Letters, vol. 3, no. 4, pp. 333-336, 2014.##
[14] S. Keshvari, S. Bejani, A. R. Keshvari, and M. Abbasi, “Predicting the Level of Combatants’ Readiness to Carry out Military Missions Using a Hidden Markov Model,” Journal of military Psychology, vol. 7, no. 27, pp. 21-39, Autumn, 2016 (In Persian).##
[15] L. Rabiner and B. H. Juang, “An introduction to hidden Markov models,” IASSP, vol. 3, no. 1, pp. 1-4, 1986##.
[16] D. Aberdeen, S. Thiebaux, and L. Zhang, “Decision Theoretic Military Operations Planning,” In International Conference on Automated Planning and Scheduling (ICAPS), pp. 402-412, 2004.##
[17] Y. K. Kevin, R. Mitchell, M. Solomon, and N. Lam, “Time Latency of Information in Networked Operations: Effect of “Human in the Loop,” 2008.##
[18] R. A. Howard, “Dynamic Programming and Markov Processes,” the M.I.T. Press, 1960.##
[19] M. Naghian Fesharaki, S. Sadati, A. H. Momeni Azndarian, and S. M. Hosseini, “Design Online Collaborative Planning Service Based on Markov Process in Command and Control Domain,” Advanced Defense Sci. & Tech. vol. 7, pp. 147-159, 2017 (In Persian).##
[20] A. J. Viterbi, “Error bounds for convolutional codes and an asymptotically optimal decoding algorithm,” IEEE Trans Inf. Theory IT, vol. 13, no. 3, pp. 260-269, 1967.##
[21] A. Babakura, MN. Sulaiman, N. Mustapha, and T. Perumal, “HMM-based decision model for smart home environment,” International Journal of Smart Home, vol. 8, no.1, pp. 129-138, 2014.##
[22] F. Madadizadeh, M. Montazeri, and A. Bahrampour, “Predicting of liver disease using Hidden Markov Model,” RJMS, vol. 23, no. 146, pp. 66-74, 2016 (In Persian).##
 
 

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History

  • Receive Date: 26 February 2018
  • Accept Date: 27 May 2018
  • Publish Date: 22 May 2019

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