Type-2 Fuzzy Extended Kalman Filter for Dynamic Security Monitoring Based on Novel Sensor Fusion


In this paper, we have focused on several relevant sensors [Laser (for speed measurements), Sonar (for space scanning) and RF (for access rights)] to cooperate in monitoring the security status of multiple dynamic agent in surveillance area. Such coordination is achieved by employing novel concepts of sensors similarity and complementarity. Furthermore, this system is aided with Extended Kalman Filter (EKF) in order to estimate the agent’s non-linear movement. Finally, transforms system state to be able to make a security suspiciousness decision by using type-2 fuzzy logic system to handle uncertainty. It is shown that the system performance can exhibit promising improvements for this dynamic security monitoring application.

Share and Cite:

T. Dakhlallah, M. Zohdy and O. Salim, "Type-2 Fuzzy Extended Kalman Filter for Dynamic Security Monitoring Based on Novel Sensor Fusion," Journal of Intelligent Learning Systems and Applications, Vol. 4 No. 3, 2012, pp. 159-168. doi: 10.4236/jilsa.2012.43016.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] M. Zohdy, A. Khan, “Global Optimization of Stochastic Multivariable Functions,” American Control Conference, San Francisco, 2-4 June 1993, p. 2339.
[2] H. Hujun and Z. Yaning, “Multi-Source Data Fusion Technology and Its Application in Geological and Mineral Survey,” 2010 2nd International Conference on Information Engineering and Computer Science (ICIECS), Wuhan, 25-26 December 2010, pp. 1-6.
[3] D. L. Hall and J. Llinas, “An Introduction to Multisensor Data Fusion,” Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, 31 May-3 June 1998, pp. 537-540.
[4] Q. Wu, D. Ferebee, Y. Lin and D. Dasgupta, “An Integrated Cyber Security Monitoring System Using Correlation-Based Techniques,” IEEE International Conference on System of Systems Engineering 2009, Albuquerque, 30 May-3 June 2009, pp. 1-6.
[5] R. Tenney and N. Sandell, “Detection with Distributed Sensors,” IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-17, No. 4, 1981, pp. 501-510.
[6] T. Dakhlallah, M. Zohdy and O. Salim, “Application of Hyper-Fuzzy Logic Decisions for A Security Monitoring System,” 2011 3rd International Conference on Computer and Automation Engineering (ICCAE 2011), 2011, p. V1-387.
[7] LV-MaxSonar-EZ0, “High Performance Sonar Range Finder,” MaxBotix Inc., 2005.
[8] CSI 430, “SpeedVueTM Laser Speed Sensor,” Emerson Process Management, 2009.
[9] MiniVLS nL, “Series Optical Speed/Phase Sensors VLS nL,” Compact Instruments.
[10] Tag-it HF-I Standard, 13.56 MHZ, “Transponder Inlays, ISO/IEC 15693 and ISO/IEC 18000-3 Global Open Standards,” Texas Instruments, 2005.
[11] Smart Card SCC-3, “3.125khz+UHF EPC GEN2,” Rui Yue RFID Co., 2006.
[12] Albert Leon-Garcia, “Probability and Random Processes for Electrical Engineering,” 2nd Edition, Pearson/Prentice Hall, Upper Saddle River, 2008.
[13] G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” University of North Carolina at Chapel Hill, Chapel Hill, 2001.
[14] E. Ayachi, S. Ihsen and B. Mohamed, “A Comparative Study of Nonlinear Time-Varying Process Modeling Techniques: Application to Chemical Reactor,” Journal of Intelligent Learning Systems and Applications, Vol. 4, No. 1, pp. 20-28.
[15] R. C. Luo and C. Yih, “Multisensor Fusion and Integration: Approaches, Applications and Future Research Directories,” IEEE Sensors Journal, Vol. 2, No. 2, 2002, pp. 107-119.
[16] O. Castilo and P. Mellin, “Type-2 Fuzzy Logic: Theory and Applications,” Tijuana Institute of Technology, Division of Graduate Studies, Vol. 223, 2008.
[17] Q. Liang and J. Mendel, “Interval Type-2 Fuzzy Logic Systems: Theory and Design,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 5, 2000, pp. 535-550.
[18] J. Mendel, “Uncertain Rule-based fuzzy logic systems: Introduction and New Directions,” Prentice-Hall, Upper Saddle River, 2001.
[19] H. Youpeng, L. Lin and Z. Yongfeng, “A Hetrogenous Sensors Track-to-Track Correlation Algorithm Based on Fuzzy Numbers Similarity Degree,” Second International Conference on Information and Computing Science, 2009 Manchester, 21-22 May 2009, pp. 191-194.
[20] J. R. Castro, O. Castillo and P. Melin, “An Interval Type-2 Fuzzy Logic Toolbox for Control Applications,” International Conference on Fuzzy Systems, London, 23-26 July 2007, pp. 1-6. doi:10.1109/FUZZY.2007.4295341

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.