Abstract
Radar system simulation usually needs to consider not only the modeling work of its static system structure but also the representation of dynamic behaviors. Traditionally, the standard Simulation Model Portability (SMP) published by European Space Agency is very suitable to build the structure of entities and their internal and external static relationships as well, but it shows pale to specify complex dynamic behaviors, such as that of radar system. To describe the behavioral aspect, we applied Synchronous data flow (SDF) and combined SMP with it. If so, one can benefit from a loose coupling way to design radar system in two different kinds of formalisms, and can simultaneously support structural modeling and behavioral modeling. For this purpose, the goal of this paper is to design a mechanism of how to combine and use these two formalisms together to form a novel simulation language. As a proof of concept, we built a set of functional models of a general radar system by using this new language and simulation results show its proper availability.
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References
Taylor, S.J.E., Khan, A., Morse K.L., et al.: Grand challenges on the theory of modeling and simulation. In: Proceedings of the Symposium on Theory of Modeling & Simulation - DEVS Integrative M&S Symposium, pp. 1–8. Society for Computer Simulation International, San Diego, CA (2013).
Lei, Y. L., Zhu, Z., & Li, Q. (2020). An ontological metamodeling framework for semantic simulation model engineering. Journal of Systems Engineering and Electronics, 31(3), 527–538.
Tolk, A (2015) The next generation of modeling & simulation: integrating big data and deep learning. In: Proceedings of the Conference on Summer Computer Simulation, Society for Computer Simulation International, San Diego, CA, 1–8
IEEE Computer Society, https://standards.ieee.org/standard/1516-2010.html, last accessed 2020/8/28.
ESA, http://www.eurosim.nl/support/manuals/manual_4_2/pdf/SMP_2.0_Metamodel-1.2.pdf, last accessed 2020/8/28.
Kim, B. S., Kang, B. G., Choi, S. H., et al. (2017). Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system. Simulation, 93(7), 579–594.
Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000). Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems (2nd ed.). . New York: Academic Press.
Tendeloo, Y.V., Vangheluwe, H.S (2017) Classic DEVS modelling and simulation. Winter Simulation Conference, Society of International Modeling and Simulation, Las Vegas, Nevada. pp. 644–658
Azar, M.C (2003) Assessing the Treatment of Airborne Tactical High Energy Lasers in Combat Simulations. MS Thesis, Air Force Institute of Technology, Dayton, OH
Miller, J.O., Jason, L., Honabarger, B.: Modeling and Measuring Network Centric Warfare (NCW) With the System Effectiveness Analysis Simulation (SEAS). In: 11th International Command and Control Research & Technology Symposium, pp.1–12 (2006).
Pidd, M. (2009). Tools for Thinking-Modeling in Management Science. New York: Wiley.
Thierry, A.S., Bastien, P., Vittori, E., et al.: “Smart Entity”-How to Build DEVS Models from Large Amount of Data and Small Amount of Knowledge?”. In: Proceedings of Simulation Tools and Techniques, pp. 615–626. Springer, Chengdu, China (2019).
Grieves, M. W. (2005). Product lifecycle management: the new paradigm for enterprises. International Journal of Product Development, 2(1–2), 71–84.
Deist, T., Patti, A., Wang, Z., et al. (2018). Simulation assisted machine learning. Bioinformatics, 35(20), 1–11.
Atkinson, C., & Kuhne, T. (2003). model-driven development: a metamodeling foundation. IEEE Software, 20(5), 36–41.
Zhu, Z., lei, Y. L., Zhu, N., & Zhu, Y. F. (2014). Composable modeling frameworks for networked air & missile defense systems. Journal of National University of Defense Technology, 36(5), 186–190.
Zhu, Z., Lei, Y. L., & Zhu, Y. F. (2020). Model-driven combat effectiveness simulation systems engineering. Defense Science Journal, 70(1), 54–59.
Addazi, L., & Ciccozzi, f. (2016). On seamless blended graphical and textual modeling for uml profiles. IEEE Access, 4(1), 1–18.
Abouzahra, A., Sabraoui, A., & Afdel, K. (2018). A practical approach for extending dsmls by composing their metamodels. Advances in Science, Technology and Engineering Systems Journal, 3(6), 358–371.
Chen, Y. Z., & Zhang, P. (2018). Modeling and simulation oriented to the multi-military mission of U.S. army[J]. Journal of Command and Control, Malvern, UK, 4(2), 89–94.
Zhang, L., Zhang, X. S., Song, X., et al. (2011). Model engineering for complex system simulation. Journal of System Simulation, 11, 221–228.
Zhang, L., & Zhou, L. F. (2018). Modeling & simulation technology in manufacturing. Journal of System Simulation, 30, 1997–2012.
Acknowledgements
We gratefully acknowledge the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) (NO. CEMEE2020K0302A) and National Natural Science Foundation of China (NO. 62003359) for supporting this research.
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Zhu, N., Zhu, Z., Lei, Y. et al. Extending SMP2 for behavioral modeling based on synchronous data flow. Wireless Netw 27, 4343–4352 (2021). https://doi.org/10.1007/s11276-021-02648-5
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DOI: https://doi.org/10.1007/s11276-021-02648-5