  Since Kelly's pioneering work on GLRT-based adaptive detection, many
solutions have been proposed to enhance either selectivity or robustness of
radar detectors to mismatched signals. In this paper such a problem is
addressed in a different space, called CFAR feature plane and given by a
suitable maximal invariant, where observed data are mapped to clusters that can
be analytically described. The characterization of the trajectories and shapes
of such clusters is provided and exploited for both analysis and design
purposes, also shedding new light on the behavior of several well-known
detectors. Novel linear and non-linear detectors are proposed with diversified
robust or selective behaviors, showing that through the proposed framework it
is not only possible to achieve the same performance of well-known receivers
obtained by a radically different design approach (namely GLRT), but also to
devise detectors with unprecedented behaviors: in particular, our results show
that the highest standard of selectivity can be achieved without sacrifying
neither detection power under matched conditions nor CFAR property.
