  The paper addresses the design of adaptive radar detectors having desired
behavior, in Gaussian disturbance with unknown statistics. Specifically, given
detection probability specifications for chosen signal-to-noise ratios and
steering vector mismatch levels, a methodology for the optimal design of
customized CFAR detectors is devised in a suitable feature plane based on
maximal invariant statistics. To overcome the analytical and numerical
intractability of the resulting optimization problem, a novel general
reduced-complexity algorithm is developed, which is shown to be effective in
providing a close approximation of the desired detector. The proposed approach
solves the open problem of ensuring a prefixed false alarm probability while
controlling the behavior under both matched and mismatched conditions, so
enabling the design of fully customized adaptive CFAR detectors.
