Calculation of the Combined Threat of Air Targets Using Neuro-Fuzzy Systems

Document Type : Original Article

Authors

1 Lecturer, Khatam Al-Anbia University of Air Defense, Tehran, Iran

2 Assistant Professor, Khatam Al-Anbia University of Air Defense, Tehran, Iran

Abstract

In recent years, intelligent structures play the key role in different military fields and take the place of human operators in novel armed services step by step. Threat evaluation of flying air targets in command and control systems is performed by expert operators through their knowledge and experience. Analysis of input data in the data-fusion systems is a very difficult task requiring complicated decision. Capability and accuracy of intelligent systems for threat prediction of flying air targets base on different received parameters can be a great assist in final macking decision. In this study, a neural network and ANFIS regression models are used to determine the priority of the threat of flying air targets in the command and control systems intelligently and instantly. The error of trained neural network and ANFIS for test dataset are 4.14% and 1.65%, respectively indicating superior ability of these structures in threat estimation of flying air targets. Furthermore, relationships among target variables and threat level is studied. Finally, a dynamic battle scene with different flying air targets is simulated and developed moled is validated.

Keywords

Smiley face

References

[1]    Z.D. Xu, Y.Q. Guo, J.T. Juo, X.C. Zhung, “Intelligent Vibration Control in Civil Engineering Structures”, Sciencedirect , ISBN: 978-0-12-405874-3, 2015.
[2]    S.N. Vassilyev, A.Yu. Kelina, Y.I. Kudinov, F.F. Pashchenko, “Intelligent control systems”, XIIth International Symposium «Intelligent Systems», INTELS’16, 5-7 October 2016, Moscow Russia, Procedia Computer Science 103, pp. 623-628, 2017.
[3]    Y. Zhao, “Intelligent Control Technology Application Based on Wireless Sensor Networks,” JDCTA, Vol. 6, No. 23, doi: 10.4156/jdcta.vol6.issue23.10, 2012.
[4]    S.M. Grath, D. Chacon, K. Whitebread, “Intelligent Mobile Agents in Military Command and Control,” http://www.airuniversity.af.mil/ website.
[5]    O. Aissa, S. Moulahoum, I. Colak, B. Babes, N. Kabache, “Design and Real Time Implementation of Three-Phase Three Switches Three Levels Vienna Rectifier Based on Intelligent Controllers,” Applied Soft Computing, S1568-4946(17)30121-7, 2017.
[6]    S. Mahapatra, R. Daniel, D. N. Dey, S.K. Nayak, “Induction Motor Control Using PSO-ANFIS,” International Conference on Intelligent Computing, Communication & Convergence, doi: 10.1016/j.procs.2015.04.212,2015.
[7]    A. Pandey ,S. Kumar,K. K. Pandey ,D. R. Parhi, “Mobile robot navigation in unknown static environments using ANFIS controller,” Perspectives in Science, http://dx.doi.org/10.1016/j.pisc.2016.04.094,2016.
[8]    E. Azimi Rad, S. Eghbali, J. Hadadnia, & A. Izadipour, “Design of an optimal and robust fuzzy model for measuring the degree of threat to moving targets,” 14th Iranian Fuzzy Systems Conference, Tabriz, 2014. (in Persian)
[9]    E. Azimirad, J. Haddadnia, “Target threat assessment using fuzzy sets theory’’, International Journal of Advances in Intelligent Informatics, Vol. 1, No. 2, pp. 57-74, 2015.
[10]  P. Tahmasebi, A. Hezarkhani, “Application of Adaptive Neuro- Fuzzy Inference System for Grade Estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran”, Australian Journal of Basic and Applied Sciences, 4(3), 408-420, 2011.
[11]  S. Tala,  M. akbari sani, & M.R. Hassani Ahangar, “Identifying Radar Targets using the GMDH Deep Neural Network,” Journal of Radar,  vol. 8, no. 1, pp. 65-74, 2020. (in Persian)
[12]  T.P. Mote, & S.D. Lokhande, “Temperature Control System Using ANFIS,” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, vol. 2, no. 1, 2012.
[13]   M. Bayat, M. Moradi, & J.Mazloum , “The Presentation of an Algorithm for Interference Detection in the Synthetic Aperture Radar,” Journal of Radar,  vol. 9, no. 1, pp. 107-117, 2021. (in Persian).
Radar
Volume 9, Issue 2 - Serial Number 26
November 2022
Pages 69-78

Files

History

  • Receive Date: 08 November 2021
  • Revise Date: 10 October 2022
  • Accept Date: 16 October 2022

Share

How to cite

Statistics