This paper evaluates the applicability of an innovative strategy for applying compressed Sensing (CS) on Synthetic Aperture Radar (SAR) imaging, in the mm-wave range, using prior or structural side information. The studied technique adds the side information to the conventional CS minimization problem using an l1-l1 minimization approach, allowing for lower sub-Nyquist sampling than standard CS predicts. The applicability of this strategy on ultra-wideband SAR measurements is tested through simulations and real Non-Destructive Testing (NDT) experiments on a 3D-printed polymer object.