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Radioelectronics and Communications Systems

, Volume 62, Issue 4, pp 143–160 | Cite as

Performance of Novel Versions of CFAR Detection Schemes Processing M-Correlated Sweeps in Presence of Interferers

  • Mohamed Bakry El MashadeEmail author
Article
  • 16 Downloads

Abstract

The detection of moving target (MTI) against clutter background represents one of the most important goals of a radar system. To achieve this objective,itisnecessarytosuppress or cancel the clutter returns with as small suppression of the target signal as possible. In this regard, MTI radar is capable of detecting such type of targets in the presence of interferers. Radar MTI is of great interest in civil and military applications, where it reduces the returns from stationary or slowly moving clutter. Additionally, in order to make decisions on the target presence, the MTI processing may be applied with automatic detection. In this situation, the CFAR detection is a common style of adaptive algorithms employed in radar systems to detect target returns against a background of noise, clutter and interference. However, the presence of MTI complicates the analysis of the detection system performance since its output sequence is correlated even though its input sequence may be uncorrelated. Our goal in this paper is to analyze the performance of a radar signal processor that consists of a nonrecursive MTI followed by a square-law integrator and a new version of CFAR circuit detection; the operation of which is based on the hybrid combination of CA and TM algorithms. The processor performance is evaluated for the case where the background environment is assumed to be ideal (homogeneous) as well as in the presence of spurious target returns amongst the contents of the reference cells. The numerical results exhibit that the processor performance can be enhanced through either increasing the number of incoherently integrated pulses or decreasing the correlation among consecutive sweeps, given that the rate of false alarm is keeping constant.

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References

  1. 1.
    G. M. Dillard, J. T. Rickard, “Performance of an MTI followed by incoherent integration for nonfluctuating signals,” Proc. of IEEE Int. Radar Conf., 28–30 Apr. 1980, San Diego, USA (IEEE, 1980), pp. 194–199.Google Scholar
  2. 2.
    M. B. El Mashade, “Performance analysis of the OS family of CFAR schemes with incoherent integration of M-pulses in the presence of interferers,” IEE Proc. Radar, Sonar Navig. 145, No. 3, 181 (June 1998). DOI:  https://doi.org/10.1049/ip-rsn:19981607.CrossRefGoogle Scholar
  3. 3.
    Jose Raul Machado-Fernandez, Norelys Mojena-Hernandez, J. C. Bacallao-Vidal, “Evaluation of CFAR detectors performance,” Iteckne 14, No. 2, 170 (2017). DOI:  https://doi.org/10.15332/iteckne.v14i2.1772.CrossRefGoogle Scholar
  4. 4.
    M. B. El Mashade, “M-correlated sweeps performance analysis of mean-level CFAR processors in multiple target environments,” IEEE Trans. Aerospace Electronic Systems 38, No. 2, 354 (Apr. 2002). DOI:  https://doi.org/10.1109/TAES.2002.1008971.CrossRefGoogle Scholar
  5. 5.
    Md. Maynul Islam, Mohammed 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 Sci. Applications 9, No. 2, 90 (2018). DOI:  https://doi.org/10.14569/IJACSA.2018.090214.Google Scholar
  6. 6.
    M. B. El Mashade, “M-correlated sweeps performance analysis of adaptive detection of radar targets in interference-saturated environments,” Ann. Telecommun. 66, No. 11–12, 617 (2011). DOI:  https://doi.org/10.1007/s12243-010-0236-5.CrossRefGoogle Scholar
  7. 7.
    W. Q. Wang, Radar Systems: Technology, Principles and Applications (Nova Science Publishers, Inc, 2013).Google Scholar
  8. 8.
    Ajay Kumar Yadav, Laxmi Kant, “Moving target detection using VI-CFAR algorithm on MATLAB platform,” Int. J. Advanced Research Computer Science and Software Engineering 3, No. 12, 915 (Dec. 2013).Google Scholar
  9. 9.
    Chang J. Kim, “A new formula to predict the exact detection probability of a generalized order statistics CFAR detector for a correlated Rayleigh target,” ETRI J. 16, No. 2, 15 (1994). DOI:  https://doi.org/10.4218/etrij.94.0194.0022.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Dejan Ivković, Milenko Andrić, Bojan Zrnić, “A new model of CFAR detector,” Frequenz 68, Nos. 3–4, 125 (Mar. 2014). DOI:  https://doi.org/10.1515/freq-2013-0087.Google Scholar
  11. 11.
    Amritakar Mandal, Rajesh Mishra, “An adaptive clutter suppression technique for moving target detector in pulse Doppler radar,” Radioengineering 23, No. 1, 84 (Apr. 2014). URI: https://www.radioeng.cz/fulltexts/2014/14 01 0084 0095.pdfGoogle Scholar
  12. 12.
    Yuhua Qin, Huili Gong, Ting Liu, “A new CFAR detector based on automatic censoring cell averaging and cell averaging,” TELKOMNIKA 11, No. 6, 3298 (2013). DOI:  https://doi.org/10.11591/telkomnika.v11i6.2686.CrossRefGoogle Scholar
  13. 13.
    Subhankar Shome, Rabindra Nath Bera, Samarendra Nath Sur, Rabi Adhikary, “Moving target detection and Doppler extraction using digital spread spectrum radar,” Int. J. Intelligent Systems Applications 6, No. 10, 47 (2014). DOI:  https://doi.org/10.5815/ijisa.2014.10.07.CrossRefGoogle Scholar
  14. 14.
    M. B. El Mashade, “Heterogeneous performance evaluation of sophisticated versions of CFAR detection schemes,” Radioelectron. Commun. Syst. 59, No. 12, 536 (2016). DOI:  https://doi.org/10.3103/S0735272716120025.CrossRefGoogle Scholar
  15. 15.
    M. B. El Mashade, “Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ2-targets,” J Systems Engineering Electronics 29, No. 1, 1 (Feb. 2018). DOI:  https://doi.org/10.21629/JSEE.2018.01.01.CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.Al-Azhar UniversityCairoEgypt

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