Fuzzy CFAR Detectors for MIMO Radars in Homogeneous and Non-Homogeneous Pareto Clutter

Abstract

In this paper, we propose to use fuzzy fusion rules to improve the performances of the CA-CFAR and OS-CFAR detectors for non-coherent MIMO radars in homogenous and non-homogenous Pareto background. First, the membership function for each individual detector is computed. At the fusion center, the membership functions are combined using four fuzzy fusion rules, namely; the “MIN”, “MAX”, “algebraic product” and the “algebraic sum” to yield a binary decision after defuzzification. The obtained results show that for a number of nodes equal to four, the performance is the best for a high number of receivers. In homogeneous background, the “algebraic product” fusion rule gives the best result when SCR > 4 dB whereas the “algebraic sum” is the best when SCR < 4 dB for the CA-CFAR. For the OS-CFAR, it is clearly shown that the “algebraic product” is the best. In non-homogenous case, the “algebraic sum” fusion rule gives the best results for the CA-CFAR, and the “algebraic product” for the OS-CFAR.

This is a preview of subscription content, access via your institution.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.

REFERENCES

  1. 1

    L. I. Ponomarev, and A. I. Skorodumov, J. Commun. Technol. Electron. 54, 76 (2009).

    Article  Google Scholar 

  2. 2

    C. Arunachalaperumal, C. Arun, S. Dhilipkumar, and G. Abija, J. Commun. Technol. Electron. 59, 1247 (2014).

    Article  Google Scholar 

  3. 3

    G. V. Kulikov, S. S. Tambovskii, Y. I. Savvateev, and Y. A. Grebenko, J. Commun. Technol. Electron. 64, 133 (2019).

    Article  Google Scholar 

  4. 4

    J. Li and P. Stoica, MIMO Radar Signal Process (Wiley, New Jersey, 2009).

    Google Scholar 

  5. 5

    E. Fishler et al., IEEE Trans. Signal Process. 54, 823 (2006).

    Article  Google Scholar 

  6. 6

    S. Zhou, H. Liu, Y. Zhao, and L. Hu, Signal Process. 91, 269 (2011).

    Article  Google Scholar 

  7. 7

    K. Yılmaz and B. Baykal, Digit. Signal Process. 25, 51 (2014).

  8. 8

    J. Li and P. Stoica, IEEE Signal Process. Mag. 24, 106 (2007).

    Article  Google Scholar 

  9. 9

    B. Friedlander, Digit. Signal Process. 23, 712 (2013).

    MathSciNet  Article  Google Scholar 

  10. 10

    Z. Xiang, B. Chen, and M. Yang, Digit. Signal Process. 65, 19 (2017).

    MathSciNet  Article  Google Scholar 

  11. 11

    H. M. Finn and R. S. Johnson, RCA Rev. 29, 414 (1968).

  12. 12

    V. G. Hansen, in Proc. IEE Int. Radar Conf. Present and Future, Oct. 23–25, 1973, (IEE, London, 1973), pp. 325–332.

  13. 13

    G. V. Trunk, IEEE Trans. Aerosp. Electron. Syst. 14, 750 (1978).

    Article  Google Scholar 

  14. 14

    H. Rohling, IEEE Trans. Aerosp. Electron. Syst. 19, 608 (1983).

    Article  Google Scholar 

  15. 15

    M. Barkat and P. K. Varshney, IEEE Trans. Aerosp. Electron. Syst. 25, 141 (1989).

    Article  Google Scholar 

  16. 16

    S. W. Leung, W. Minett, Y. M. Siu, and M. K. Lee, IEEE Trans. Aerosp. Electron. Syst. 38, 346 (2002).

    Article  Google Scholar 

  17. 17

    Z. Hammoudi and F. Soltani, IEE Proc. F, 151, 135 (2004).

    Google Scholar 

  18. 18

    H. A. Meziani and F. Soltani, Signal Process. 91, 2530 (2011).

    Article  Google Scholar 

  19. 19

    A. Zaimbashi, M. R. Taban, M. M. Nayebi, and Y. Norouzi, Signal Process. 88, 558 (2008).

    Article  Google Scholar 

  20. 20

    H. Ghahramani, N. Parhizgar, B. A. Arand, and M. Barari, J. Commun. Technol. Electron. 65, 160 (2020).

    Article  Google Scholar 

  21. 21

    M. Baadeche and F. Soltani, “Analysis of the fuzzy greatest of CFAR detector in homogeneous and non-homogeneous Weibull clutter,” in Proc. 8th Int. Conf. on Machine Vision, (ICMV 2015), Barcelona, Dec. 8, 2015, p. 1; (SPIE Proc. 9875, 98751X (2015).

  22. 22

    N. Janatian, M. Modarres-Hashemi, and A. Sheikhi, Circuits Syst. Signal Process. 32, 1389 (2013).

    Article  Google Scholar 

  23. 23

    M. Baadeche and F. Soltani, Digit. Signal Process. 44, 47 (2015).

    Article  Google Scholar 

  24. 24

    I. Chalabi, A. Mezache, F. Soltani, and F. Khaldi “CFAR detectors for MIMO radars in a Pareto background,” in Sem. on Detection Systems Architectures and Technologies (DAT), Algiers, Feb. 20–22, 2017 (IEEE, New York, 2017), p. 1.

  25. 25

    G. V. Weinberg, IET Radar Sonar Navig. 7, 153 (2013).

    Article  Google Scholar 

  26. 26

    L. Rosenberg and G. V. Weinberg, “Performance analysis of Pareto CFAR detectors,” in Proc. IET Int. Conf. on Radar Systems, Belfast, Oct. 23–26, 2017 (IEEE, New York, 2017), p. 1.

  27. 27

    G. V. Weinberg, Signal Process. 104, 264–273 (2014).

    Article  Google Scholar 

  28. 28

    G. V. Weinberg, L. Bateman, and P. Hayden, Digit. Signal Process. 68, 192 (2017).

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to F. Soltani.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Khaldi, F., Soltani, F. & Baadeche, M. Fuzzy CFAR Detectors for MIMO Radars in Homogeneous and Non-Homogeneous Pareto Clutter. J. Commun. Technol. Electron. 66, 62–69 (2021). https://doi.org/10.1134/S1064226921010046

Download citation

Keywords:

  • MIMO Radars
  • Fuzzy rules
  • non-homogeneous clutter
  • CFAR detection