Radar sliding window detection processes are often used in signal processing as alternatives to Neyman-Pearson based decision rules, due to the fact that they have a simpler receiver implementation and can often be designed to maintain a constant false alarm rate in homogeneous clutter. These detection processes produce a measurement of the clutter level from a series of observations, and compare a normalised version of this to a cell under test. The latter is an amplitude squared measurement of the signal plus clutter in the complex domain. It has been suggested by some authors that that there is sufficient merit in the approximation of the cell under test by a distributional model similar to that assumed for the clutter distribution. This is certainly the case when a Gaussian target is combined with Gaussian clutter, or equivalently a Swerling 1 target and exponentially distributed intensity clutter. The purpose of the current paper is to demonstrate, in a modern maritime surveillance radar context where the clutter is modelled by Pareto statistics, that such an approximation is only valid under certain limiting conditions.
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