Simulation for the Reverse Extrapolation of Radar Threats and their Verification

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Author(s)

Sanguk Noh 1,* So Ryoung Park 2

1. School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea

2. School of Information, Communications, and Electronics Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2019.07.01

Received: 11 Jan. 2019 / Revised: 20 Feb. 2019 / Accepted: 19 Mar. 2019 / Published: 8 Jul. 2019

Index Terms

Modeling and Simulation of Electronic Warfare, Machine Learning, Dempster-Shafer Theory, Intelligent Recommendation of Radars

Abstract

Various and unpredictable electronic warfare situations drive the development of an integrated electronic warfare (EW) simulator that can perform electronic warfare modeling and simulation on radar threats. This paper introduces the basic components of simulation system that enables our agents to be operational in EW settings. In various simulation of EW environments, our agents can preset their path in the existence of enemy radars' surveillance and autonomously be aware of radar threats while they proceed in their own route. As reversely extrapolating radar threats given radio-active parameters received, our agents perform an appropriate jamming technique in order to deceive the enemy radar keeping track of our agents. Based upon the response of the radar threat attacked by the jamming techniques, our agents figure out the types of the radar threat and verify its identification. For the actual and helpful information, real radars with the probability of similarity could be prioritized from radar database. The integrated EW simulator that we have designed and developed in this paper enables our agents to perform such capabilities as reverse extrapolation of RF threats, its verification using jamming, and recommendation of similar radars, and to evaluate their autonomous behaviors in a tapestry of realistic scenarios.

Cite This Paper

Sanguk Noh, So Ryoung Park, "Simulation for the Reverse Extrapolation of Radar Threats and their Verification", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.7, pp.1-9, 2019. DOI:10.5815/ijisa.2019.07.01

Reference

[1]A.E. Spezio, "Electronic warfare systems," IEEE Transactions on Microwave Theory and Techniques, vol. 50, no. 3, pp. 633-644, 2002.
[2]D.J. Bryant, F.M.J. Lichacz, J.G. Hollands and J.V. Baranski, "Modeling Situation Awareness in an Organizational Context: Military Command and Control," in A cognitive approach to situation awareness: theory and application, eds. S. Banbury and S. Tremblay, Burlington, VT: Ashgate Publishing Company, Chapter 6, 2004.
[3]J. Thangarajah, L. Padgham, and S. Sardina, "Modelling situations in intelligent agents," in Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (AAMAS '06), pp. 1049-1051, New York, NY, USA, 2006.
[4]S. Noh and S.R. Park, "Reverse modeling and autonomous extrapolation of RF Threats," International Journal on Advances in Computer Science, vol. 4, no. 18, pp. 89-97, Nov. 2015.
[5]G. Shafer, A mathematical theory of evidence, Princeton University Press, 1976.
[6]Q. Chen, A. Whitbrook, U. Aickelin and C. Roadknight, "Data classification using the Dempster–Shafer method," Journal of Experimental and Theoretical Artificial Intelligence, vol. 26, no. 4, pp. 493-517, 2014.
[7]J. Patrick and N. James, "A Task-Oriented Perspective of Situation Awareness," in A cognitive approach to situation awareness: theory and application, eds. S. Banbury and S. Tremblay, Burlington, VT: Ashgate Publishing Company, Chapter 4, 2004.
[8]S. Kumar Das, "Modeling intelligent decision-making command and control agents: an application to air defense," IEEE Intelligent Systems, vol. 29, no. 5, pp. 22-29, 2014.
[9]Z. Han, "Modeling method and application of multi-agents in armored force operation simulation," in Proceedings of Advanced Information Technology, Electronic and Automation Control conference, pp. 2046-2049, Chongqing, China, Mar. 2017.
[10]B. Barshan and B. Eravci, "Automatic radar antenna scan type recognition in electronic warfare," IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 4, pp. 2908-2931, Oct. 2012.
[11]M. McDonald and D. Cerutti-Maon, "Multi-phase centre coherent radar sea clutter modelling and simulation," IET Radar, Sonar, and Navigation, vol. 11, no. 9, pp. 1359-1366, Aug. 2017.
[12]S.R. Park, I. Nam, and S. Noh, "Modeling and simulation for the investigation of radar responses to electronic attacks in electronic warfare environments," Security and Communication Networks, ID 3580536, 13 pages, 2018.
[13]B.R. Mahafza, Radar Systems Analysis and Design Using Matlab, 3rd Ed., CRC Press, 2012.
[14]D.L. Adamy, EW 101: A First Course in Electronic Warfare, Artech House, 2015.
[15]R. Poisel, Modern Communications Jamming Principles and Techniques, Artech House, 2011.
[16]J.D. Townsend, M.A. Saville, S.M. Hongy, and R.K. Martin, "Simulator for velocity gate pull-off electronic countermeasure techniques," in Proceedings of the IEEE Radar Conference, pp. 1-6, Rome, Italy, May 2008.
[17]L. Surendra, S. Shameem, N. Susmitha, and T.S. Ram, "Analysis of self-screening jammer parameters with radar equation", International Journal of Engineering Research and Applications, vol. 4, no. 3, pp. 205-207, Mar. 2014.
[18]I.H. Witten, E. Frank and M.A. Hall, Data Mining: Practical machine learning tools and techniques, 3rd edition, Morgan Kaufmann Publishers, 2011.
[19]Q. Yang and X. Wu, "10 Challenging Problems in Data Mining Research," International Journal of Information Technology and Decision Making, vol. 5, no. 4, pp. 597-604, 2006.