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Performance evaluation of CA-, GO- and SO-CFAR processors in a non-centered Lévy-distributed clutter.

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Title: Performance evaluation of CA-, GO- and SO-CFAR processors in a non-centered Lévy-distributed clutter.
Authors: Meftah, El-Hadi1 elhadi.meftah@usthb.edu.dz, Rabehi, Abdelhalim2, Benmahmoud, Slimane3
Source: Majlesi Journal of Electrical Engineering. Jun2025, Vol. 19, p1-7. 7p.
Subjects: Constant false alarm rate (Data processing), Clutter (Radar), Monte Carlo method, Random noise theory, Evaluation methodology, Automatic detection in radar, Lévy processes
Abstract: Constant false alarm rate (CFAR) processors are critical for radar reliable target detection in radar systems. Traditional CFAR designs often assume Gaussian clutter, which may not reflect real-world conditions. Lévy distributions, with heavy tails and a location parameter (δ), provide a more accurate model for non-Gaussian and non-centered clutter in complex environments. This paper presents a comprehensive performance analysis of three widely used CFAR processors-cell-averaging (CA), greatest-of (GO), and smallest-of (SO) in homogeneous Lévy-distributed clutter with an arbitrary δ. We derive integral-form expressions for the probability of false alarm (PFA) for each processor, explicitly incorporating δ. Furthermore, we provide analytical formulations for the probability density function (PDF) of key statistics involving Lévy random variables, such as sums, minima, and maxima. Monte Carlo simulations validate the theoretical results, showing that the PFA performance improves with increasing δ, highlighting the critical impact of clutter location on CFAR detector performance. These findings offer valuable insights for designing robust CFAR detectors in non-Gaussian, non-centered clutter environments. [ABSTRACT FROM AUTHOR]
Copyright of Majlesi Journal of Electrical Engineering is the property of OICC Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Performance evaluation of CA-, GO- and SO-CFAR processors in a non-centered Lévy-distributed clutter.
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  Data: <searchLink fieldCode="JN" term="%22Majlesi+Journal+of+Electrical+Engineering%22">Majlesi Journal of Electrical Engineering</searchLink>. Jun2025, Vol. 19, p1-7. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Constant+false+alarm+rate+%28Data+processing%29%22">Constant false alarm rate (Data processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Clutter+%28Radar%29%22">Clutter (Radar)</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Random+noise+theory%22">Random noise theory</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+detection+in+radar%22">Automatic detection in radar</searchLink><br /><searchLink fieldCode="DE" term="%22Lévy+processes%22">Lévy processes</searchLink>
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  Data: Constant false alarm rate (CFAR) processors are critical for radar reliable target detection in radar systems. Traditional CFAR designs often assume Gaussian clutter, which may not reflect real-world conditions. Lévy distributions, with heavy tails and a location parameter (δ), provide a more accurate model for non-Gaussian and non-centered clutter in complex environments. This paper presents a comprehensive performance analysis of three widely used CFAR processors-cell-averaging (CA), greatest-of (GO), and smallest-of (SO) in homogeneous Lévy-distributed clutter with an arbitrary δ. We derive integral-form expressions for the probability of false alarm (PFA) for each processor, explicitly incorporating δ. Furthermore, we provide analytical formulations for the probability density function (PDF) of key statistics involving Lévy random variables, such as sums, minima, and maxima. Monte Carlo simulations validate the theoretical results, showing that the PFA performance improves with increasing δ, highlighting the critical impact of clutter location on CFAR detector performance. These findings offer valuable insights for designing robust CFAR detectors in non-Gaussian, non-centered clutter environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Majlesi Journal of Electrical Engineering is the property of OICC Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.57647/j.mjee.2025.1902.40
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      – Code: eng
        Text: English
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        PageCount: 7
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      – SubjectFull: Constant false alarm rate (Data processing)
        Type: general
      – SubjectFull: Clutter (Radar)
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Random noise theory
        Type: general
      – SubjectFull: Evaluation methodology
        Type: general
      – SubjectFull: Automatic detection in radar
        Type: general
      – SubjectFull: Lévy processes
        Type: general
    Titles:
      – TitleFull: Performance evaluation of CA-, GO- and SO-CFAR processors in a non-centered Lévy-distributed clutter.
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            NameFull: Meftah, El-Hadi
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            NameFull: Rabehi, Abdelhalim
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            NameFull: Benmahmoud, Slimane
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            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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