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Performance comparison of mean-level CFAR detectors in homogeneous and non-homogeneous Weibull clutter for MIMO radars

  • M. Baadeche
  • F. SoltaniEmail author
  • F. Gini
Original Paper
  • 34 Downloads

Abstract

In this paper, we analyze and compare the performance of the CA-CFAR, GO-CFAR and the SO-CFAR detectors in homogeneous and non-homogeneous Weibull background with known shape parameter for Multi-Input Multi-Output radars with widely separated antennas. The non-homogeneity is represented by the presence of interfering targets and a clutter edge in the reference window. We derive closed-form expressions of the probability of false alarm of the three detectors in homogeneous environment. Detector performance in non-homogeneous environment is investigated by means of Monte Carlo simulations. The numerical results show that the best performance is obtained by the SO-CFAR in the case of a high number of interferences, whereas the GO-CFAR has the best false-alarm regulation when the number of cells contaminated by clutter exceeds half the number of reference cells.

Keywords

MIMO radar Weibull clutter CFAR detectors Non-homogeneous clutter 

Notes

References

  1. 1.
    Li, J., Stoica, P.: MIMO Radar Signal Processing. Wiley, New Jersey (2009)Google Scholar
  2. 2.
    Chong, C.Y., et al.: MIMO radar detection in non- Gaussian and heterogeneous clutter. IEEE J. Sel. Top. Signal Process. 4(1), 115–126 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chong, C.Y.: Signal processing for MIMO radars: detection under Gaussian and non-Gaussian environments and application to STAP. Ph.D. thesis, Supelec, France (2011)Google Scholar
  4. 4.
    Baadeche, M., Soltani, F.: Performance analysis of ordered CFAR detectors for MIMO radars. Digit. Signal Process. 44, 47–57 (2015)CrossRefGoogle Scholar
  5. 5.
    Finn, H.M., Johnson, R.S.: Adaptive detection model with threshold control as a function of spatially sampled clutter-level estimates. RCA Rev. 29, 414–464 (1968)Google Scholar
  6. 6.
    Gandhi, P.P., Kassam, S.A.: Analysis of CFAR processors in nonhomogeneous background. IEEE Trans. Aerosp. Electron. Syst. 24, 427–445 (1988)CrossRefGoogle Scholar
  7. 7.
    Messali, Z., Soltani, F., Sahmoudi, M.: Robust radar detection of CA, GO and SO CFAR in pearson measurements based on a nonlinear compression procedure for clutter reduction. Signal Image Video Process. 2, 169–176 (2008)CrossRefGoogle Scholar
  8. 8.
    Rohling, H.: Radar CFAR thresholding in clutter and multiple target situations. IEEE Trans. Aerosp. Electron. Syst. 19, 608–621 (1983)CrossRefGoogle Scholar
  9. 9.
    Abdou, L., Soltani, F.: OS-CFAR and CMLD threshold optimization in distributed systems using evolutionary strategies. Signal Image Video Process. 2, 155–167 (2008)CrossRefGoogle Scholar
  10. 10.
    Elias-Fuste, A.R., De Mercado, M.G., Davo, E.R.: Analysis of some modified order statistics CFAR: OSGO and OSSO CFAR. IEEE Trans. Aerosp. Electron. Syst. 26(1), 197–202 (1990)CrossRefGoogle Scholar
  11. 11.
    Tabet, L., Soltani, F.: A generalized switching CFAR processor based on test cell statistics. Signal Image Video Process. 3, 265–273 (2009)CrossRefGoogle Scholar
  12. 12.
    Baadeche, M., Soltani, F.: Performance comparison of some CFAR detectors in homogenous and non-homogenous clutter. In: IEEE International Conference on Signal and Image Processing Applications, Malaysia, pp. 101–105 (2013)Google Scholar
  13. 13.
    Ritcey, J.A.: Performance analysis of the censored mean level detector. IEEE Trans. Aerosp. Electron. Syst. 22(4), 443–454 (1986)CrossRefGoogle Scholar
  14. 14.
    Ozgunes, I., Gandhi, P.P., Kassam, S.A.: A variably trimmed mean CFAR radar detector. IEEE Trans. Aerosp. Electron. Syst. 28(4), 1002–1014 (1992)CrossRefGoogle Scholar
  15. 15.
    Weinberg, G.V.: Trimmed geometric mean order statistic CFAR detector for Pareto distributed clutter. Signal Image Video Process 12(4), 1–7 (2017)MathSciNetGoogle Scholar
  16. 16.
    Sammartino, P., Baker, C., Griffiths, H.: Adaptive MIMO radar system in clutter. In: IEEE Radar Conference, USA, pp. 276–281 (2007)Google Scholar
  17. 17.
    Akcakaya, M., Nehorai, A.: MIMO radar detection and adaptive design in compound-Gaussian clutter. In: IEEE Radar Conference, USA, pp. 236–241 (2010)Google Scholar
  18. 18.
    Janatian, N., Modarres-Hashemi, M., Sheikhi, A.: CFAR detectors for MIMO radars. Circuits Syst. Signal Process. 32(3), 1389–1418 (2013)CrossRefGoogle Scholar
  19. 19.
    Yilmaz, K., Baykal, B.: Multiple target localization and data association for frequency-only widely separated MIMO radar. Digit. Signal Process. 25, 51–61 (2014)CrossRefGoogle Scholar
  20. 20.
    Yue, A., Wei, Y., Blum, R. S., et al.: Cramer-Rao lower bound for multitarget localization with noncoherent statistical MIMO radar. In: IEEE Radar Conference (RadarCon), USA, pp. 1497–1502 (2015)Google Scholar
  21. 21.
    Duofang, C., Baixiao, C., Guodong, Q.: Angle estimation using esprit in MIMO radar. Electron. Lett. 44(12), 770–771 (2008)CrossRefGoogle Scholar
  22. 22.
    Fangqing, W., et al.: Angle estimation for bistatic MIMO radar in the presence of spatial colored noise. Signal Process. 134, 261–267 (2017)CrossRefGoogle Scholar
  23. 23.
    Guolong, C., et al.: Distributed target detection with polarimetric MIMO radar in compound-Gaussian clutter. Digit. Signal Process. 22(3), 430–438 (2012)MathSciNetCrossRefGoogle Scholar
  24. 24.
    He, Q., et al.: MIMO radar moving target detection in homogeneous clutter. IEEE Trans. Aerosp. Electron. Syst. 46(3), 1290–1301 (2010)CrossRefGoogle Scholar
  25. 25.
    Wang, P., Li, H., Himed, B.: Moving target detection using distributed MIMO radar in clutter with nonhomogeneous power. IEEE Trans. Signal Process. 59(10), 4809–4820 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Li, N., et al.: Adaptive detection of moving target with MIMO radar in heterogeneous environments based on Rao and Wald tests. Signal Process. 114, 198–208 (2015)CrossRefGoogle Scholar
  27. 27.
    Musha, T., Sekine, M.: Models of Clutter. Peter Peregrinus Ltd, New York (1993)CrossRefGoogle Scholar
  28. 28.
    Baadeche, M., Soltani, F.: Performance analysis of mean level constant false alarm rate detectors with binary integration in Weibull background. IET Radar Sonar Navig. 9(3), 233–240 (2015)CrossRefGoogle Scholar
  29. 29.
    Meng, X.W.: Performance analysis of ordered-statistic greatest of-constant false alarm rate with binary integration for M-sweeps. IET Radar Sonar Navig. 4(1), 37–48 (2010)CrossRefGoogle Scholar
  30. 30.
    Meng, X.W.: Performance evaluation of OSSO-CFAR with binary integration in Weibull background. J. Electron. 30(1), 83–90 (2013)Google Scholar
  31. 31.
    Meng, X.W.: Performance analysis of OS-CFAR with binary integration for Weibull background. IEEE Trans. Aerosp. Electron. Syst. 49(2), 1357–1366 (2013)CrossRefGoogle Scholar
  32. 32.
    Zhang, X., et al.: Intelligent CFAR detector for non- homogeneous weibull clutter environment based on skewness. In: IEEE Radar Conference (RadarConf18), USA, pp. 0322–0326 (2018)Google Scholar
  33. 33.
    Gini, F., Lombardini, F.: Decentralized CFAR detection with binary integration in Weibull clutter. IEEE Trans. Aerosp. Electron. Syst. 33(2), 396–407 (1997)CrossRefGoogle Scholar
  34. 34.
    Gini, F., Lombardini, F., Verrazzani, L.: On distributed signal detection with multiple local free parameters. IEEE Trans. Aerosp. Electron. Syst. 35(4), 1457–1466 (1999)CrossRefGoogle Scholar
  35. 35.
    Gini, F., Lombardini, F., Verrazzani, L.: Robust monparametric multiradar CFAR detection against non-Gaussian spiky clutter. IEE Proc. Radar Sonar Navig. 144(3), 131–140 (1997)CrossRefGoogle Scholar
  36. 36.
    Dong, Y.: Distribution of X-band High Resolution and High Grazing Angle Sea Clutter. Defense Science and Technology Organization, Edinburgh (2006)Google Scholar
  37. 37.
    Ravid, R., Levanon, N.: Maximum-likelihood CFAR for Weibull background. IEE Proc. 139(3), 256–264 (1992)Google Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratoire Signaux et Systèmes de Communication, Département d’électroniqueUniversité ConstantineConstantineAlgeria
  2. 2.Department of Ingegneria dellInformazioneUniversity of PisaPisaItaly

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