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This paper considers the design of tunable decision schemes capable ofrejecting with high probability mismatched signals embedded in Gaussianinterference with unknown covariance matrix. To this end, a sparse recoverytechnique is exploited to enhance the resolution at which the target angle ofarrival is estimated with the objective to obtain high-selective detectors. Theoutcomes of this estimation procedure are used to devise detectionarchitectures relying on either the twostage design paradigm or heuristicdesign procedures based upon the generalized likelihood ratio test. Remarkably,the new decision rules exhibit a bounded-constant false alarm rate property andallow for a tradeoff between the matched detection performance and therejection of undesired signals by tuning a design parameter. At the analysisstage, the performance of the newly proposed detectors is assessed also incomparison with existing selective competitors. The results show that the newdetectors can outperform the considered counterparts in terms of rejection ofunwanted signals, while retaining reasonable detection performance of matchedsignals.

A Sparse Learning Approach to the Design of Radar Tunable Architectures with Enhanced Selectivity Properties