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

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	2345377X
DOI:	10.57647/j.mjee.2025.1902.40