Binary integration performance analysis of CA family of CFAR strategies in homogeneous Weibull clutter




CFAR detection processors, Weibull clutter, CA adaptive detection family, binary integration, target fluctuation model


The exact knowledge of the clutter properties is of great importance for modern radar systems due to the role that they are directly play mainly through the CFAR processor design in the optimization of the detection process. Owing to the central limit theorem, the most used models are of Gaussian distribution that has the feature of simple processing architectures. Actually, these models are expected to represent, to a good extent, the sea and terrain clutters for poor-resolution and largely grazing angle radar. If any of these two practical situations is no longer satisfied, these models become insufficient for the clutter representation. Under these circumstances, the clutter becomes spikier and this successively leads to a false alarm rate behavior, which is much more important than that predicted by a Gaussian model. For that reason, other models, which are capable of taking into account these types of operating conditions, have to be investigated. This is typically needed for the case of high-resolution radars. The developed clutter models will allow us to reduce the intensity of clutter, through the reduction of the analyzed cell resolution and, consequently, the signal-to-noise ratio will be improved. Accordingly, the target detection probability should increase. Weibull represents one of the most suitable distributions for the high-resolution land clutter modeling. On the other hand, due to the simplicity of binary integrator and its robustness in non-Gaussian clutter, it has been widely used in radar detectors. This paper is interested in evaluating the detection performance of the CA (Cell-Averaging) family of CFAR schemes against clutter of Weibull distribution, with an assumption of known shape parameter, when they incorporate a binary integrator amongst their basic contents.


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