Radioelectronics and Communications Systems

, Volume 61, Issue 9, pp 377–393 | Cite as

Performance Predominance of a New Strategy for CFAR Processors over the N-P Model in Detecting Four Degrees of Freedom χ2 Fluctuating Targets

  • Mohamed B. El Mashade


Modern radars have adopted adaptive processing techniques to mitigate the deleterious effects of unwanted clutter and jammer. In this situation, the CFAR algorithms play a vital role in achieving the heterogeneous detection of fluctuating targets. In this regard, while the CA-CFAR processor has the top homogeneous performance, the OS and TM techniques have been suggested to provide robust estimates of the threshold in heterogeneous situations. In order to simultaneously exploit the merits of CA and OS or TM processors, some their hybrid versions have been recently introduced. They are termed as CAOS and CATM models. Practically, the frequency diversity between noncoherent sweeps is widespread in actual radar systems. Additionally, the pulse integration strategy is often used in radar systems to improve the target signal-to-noise ratio and correspondingly the system detection performance. For this reason, this paper is focusing on analyzing these new models in the case where the radar receiver noncoherently integrates M-pulses to handle its detection. Closed-form expression is derived for their nonhomogeneous performance. The tested as well as the spurious targets are assumed to follow χ2-distribution with four degrees of freedom in their fluctuations. Our simulation results reveal that the new version CATM exhibits a homogeneous performance that outweighs that of the classical Neyman-Pearson (N-P) procedure, which is employed as a baseline comparison for other strategies in the field of adaptive detectors.


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  1. 1.
    M. A. Weiner, “Detection probability for partially correlated chi-square targets,” IEEE Trans. Aerospace Electronic Systems 24, No. 4, 411 (1988). DOI: 10.1109/7.7183.CrossRefGoogle Scholar
  2. 2.
    M. Barkat, S. D. Himonas, P. K. Varshney, “CFAR detection for multiple target situations,” IEE Proc. F - Radar Signal Processing 136, No. 5, 193 (1989). DOI: 10.1049/ip-f-2.1989.0033.CrossRefGoogle Scholar
  3. 3.
    M. B. El Mashade, “Detection performance of the trimmed-mean CFAR processor with noncoherent integration,” IEE Proc. Radar, Sonar Navig. 142, No. 1, 18 (1995). DOI: 10.1049/ip-rsn:19951626.CrossRefGoogle Scholar
  4. 4.
    Daniel T. Nagle, Jafar Saniie, “Performance analysis of linearly combined order statistic CFAR detectors,” IEEE Trans. Aerospace Electronic Systems 31, No. 2, 522 (1995). DOI: 10.1109/7.381903.CrossRefGoogle Scholar
  5. 5.
    D.-S. Han, “Detection performance of CFAR detectors based on order statistics for partially correlated chi-square targets,” IEEE Trans. Aerospace Electronic Systems 36, No. 4, 1423 (2000). DOI: 10.1109/7.892694.CrossRefGoogle Scholar
  6. 6.
    A. Farrouki, M. Barkat, “Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments,” IEE Proc. - Radar Sonar Navig. 152, No. 1, 43 (2005). DOI: 10.1049/ip-rsn:20045006.CrossRefGoogle Scholar
  7. 7.
    M. B. El Mashade, “Analysis of cell-averaging based detectors for - 2 fluctuating targets in multitarget environments,” J. Electron. (China) 23, No. 6, 853 (2006). DOI: 10.1007/s11767-005-0067-0.CrossRefGoogle Scholar
  8. 8.
    T. Laroussi, M. Barkat, “A performance comparison of two time diversity systems using CMLD-CFAR detection for partially-correlated chi-square targets and multiple target situations,” Proc. of 14th European Signal Processing Conf., 4–8 Sept. 2006, Florence, Italy (IEEE, 2006), pp. 4–8, URI: Scholar
  9. 9.
    M. B. El Mashade, “Performance analysis of OS structure of CFAR detectors in fluctuating target environments,” PIER C 2, 127 (2008). DOI: 10.2528/PIERC08022807.CrossRefGoogle Scholar
  10. 10.
    B. Magaz, A. Belouchrani, M. Hamadouche, “A new adaptive linear combined CFAR detector in presence of interfering targets,” PIER B 34, 367 (2011). DOI: 10.2528/PIERB11012603.CrossRefGoogle Scholar
  11. 11.
    Long Cai, Xiaochuan Ma, Qi Xu, Bin Li, Shiwei Ren, “Performance analysis of some new CFAR detectors under clutter,” J. Computers 6, No. 6, 1278 (2011). DOI: 10.4304/jcp.6.6.1278-1285.CrossRefGoogle Scholar
  12. 12.
    W. Q. Wang, Radar Systems: Technology, Principles and Applications (Nova Science Publishers, Inc, 2013).Google Scholar
  13. 13.
    Dejan Ivkoviã, Milenko Andriã, Bojan Zrniã, “A new model of CFAR detector,” Frequenz 68, No. 3–4, 125 (2014). DOI: 10.1515/freq-2013-0087.Google Scholar
  14. 14.
    Dejan Ivkoviã, Milenko Andriã, Bojan Zrniã, “False alarm analysis of the CATM-CFAR in presence of clutter edge,” Radioengineering 23, No. 1, 66 (2014). URI: 0072.pdf.Google Scholar
  15. 15.
    M. B. El Mashade, “Partially-correlated2 targets detection analysis of GTM-adaptive processor in the presence of outliers,” Int. J. Image, Graphics Signal Processing 7, No. 12, 70 (2014). DOI: 10.5815/ijigsp.2014.12.10.CrossRefGoogle Scholar
  16. 16.
    Dejan Ivkoviã, Milenko Andriã, Bojan Zrniã, Predrag Okiljeviã, Nadica Koziã, “CATM-CFAR detector in the receiver of the software defined radar,” Sci. Tech. Rev. 54, No. 4, 27 (2014). URI: Scholar
  17. 17.
    S. Ahmed, “Novel noncoherent radar pulse integration to combat noise jamming,” IEEE Trans. Aerospace Electronic Systems 51, No. 3, 2350 (2015). DOI: 10.1109/TAES.2015.140315.CrossRefGoogle Scholar
  18. 18.
    Dejan Ivkoviã, Milenko Andriã, Bojan Zrniã, “Detection of very close targets by fusion CFAR detectors,” Sci. Tech. Rev. 66, No. 3, 50 (2016). DOI: 10.5937/STR1603050I.CrossRefGoogle Scholar
  19. 19.
    J. R. Machado-Fernandez, N. Mojena-Hernandez, J. C. Bacallao-Vidal, “Evaluation of CFAR detectors performance,” Iteckne 14, No. 2, 170 (2017). DOI: 10.15332/iteckne.v14i2.1772.CrossRefGoogle Scholar
  20. 20.
    M. M. Islam, M. Hossam-E-Haider, “Detection capability and CFAR loss under fluctuating targets of different Swerling model for various gamma parameters in RADAR,” Int. J. Advanced Computer Science Applications 9, No. 2, 90 (2018). DOI: 10.14569/IJACSA.2018.090214.Google Scholar
  21. 21.
    M. B. El Mashade, “Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated - 2 -targets,” J. Systems Engineering Electronics 29, No. 1, 1 (2018). DOI: 10.21629/JSEE.2018.01.01.Google Scholar

Copyright information

© Allerton Press, Inc. 2018

Authors and Affiliations

  1. 1.Al-Azhar UniversityCairoEgypt

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