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We present a compressive sensing based defect detection by multiple inputmultiple output (MIMO) wireless radar. Here, defects are inside a layeredmaterial structure, therefore, due to reflections from the surface of thelayered material structure the defect detection is challenging. By utilizing alow-rank nature of the reflections of the layered material structure and sparsenature of the defects, we propose a method based on rank minimization andsparse recovery. To improve the accuracy in the recovery of low-rank and sparsecomponents, we propose a non-convex approach based on the iterativelyreweighted nuclear norm and iteratively reweighted $\ell_1-$norm algorithm. Ournumerical results show that the proposed method is able to demix and recoverthe signalling responses of the defects and layered structure successfully fromsubstantially reduced number of observations. Further, the proposed approachoutperforms the state-of-the-art clutter reduction approaches

Defect Detection by MIMO Wireless Sensing based on Weighted Low-Rank plus Sparse Recovery