Adaptive Radar Detection of Distributed Targets under Conic Constraints

In this paper we deal with the problem of adaptive detection of mismatched mainlobe targets and/or sidelobe interfering signals that are distributed in range. More precisely, in order to improve the robustness of the decision rule in presence of mainlobe targets we investigate the impact of modeling the actual useful signal as a vector belonging to a proper cone with axis the nominal steering vector; similarly, in order to improve the rejection capabilities of the decision rule in presence of sidelobe targets we study the effects of replacing the usual noise only hypothesis with a noise plus interferers hypothesis where interferers belong to the complement of a cone with axis the nominal steering vector. Mixed solutions are also studied. At the design stage we adopt the GLRT principle to derive CFAR detectors under the assumption that training data are available and the noise vectors share one and the same Gaussian distribution (with unknown covariance matrix). The performance assessment, conducted also in comparison to other approaches, proves the effectiveness of the proposed strategies.