On the Construction of CFAR Decision Rules via Transformations

The Pareto distribution has been validated as a suitable model for X-band maritime surveillance radar clutter returns, and consequently there has been much interest in developing radar detection algorithms under such a clutter model assumption. Recent research has shown that it is possible to apply a transformation approach to adapt the traditional constant false alarm rate (CFAR) detectors, designed to operate in exponentially distributed clutter, to the Pareto setting. However, it was found that this approach resulted in the decision rule requiring a priori knowledge of the Pareto scale parameter. It is shown here that this shortcoming can be rectified by application of a complete sufficient statistic to the transformed detector. Consequently, new decision rules are derived and it is shown that they not only achieve the CFAR property but in some instances can improve the performance of the decision rules from which they are derived.