Fornaro, G.G.FornaroPauciullo, A.A.PauciulloReale, D.D.RealeWeiß, M.M.WeißBudillon, A.A.BudillonSchirinzi, G.G.Schirinzi2022-03-132022-03-132017https://publica.fraunhofer.de/handle/publica/40124310.1109/IGARSS.2017.8127499Sparse Representation and Compressive Sensing (CS) theory has gained an increasing interest during the last years in many application fields, including SAR tomography. The latter offers the possibility to perform a focusing, beyond the classical 2D (azimuth-range) domain, along other dimensions: f.i., elevation and velocity. The literature, however, lacks of an assessment of the improvement of CS over classical point cloud detection schemes based on the Generalized Likelihood Ratio test which, in the simplest form, use basic beamforming (matched filter) detection based schemes. This work aims to provide a contribution along this line.en621conference paper