2020/10/31 by Udaya S. K. P. Miriya Thanthrige, Ali Kariminezhad, Thanthrige, Udaya S. K. P. Miriya +5
Engineering · Mathematics · #Algorithm #Artificial intelligence #Channel (broadcasting) #Clutter #Compressed sensing #Computer science #Convex optimization #MIMO #Mathematics #Microwave Imaging and Scattering Analysis #Minification #Norm (philosophy) #Radar #Rank (graph theory) #Reduction (mathematics) #Regular polygon #Sparse and Compressive Sensing Techniques #Sparse approximation #Telecommunications #Ultrasonics and Acoustic Wave Propagation #Wireless #eess.SP
paper · pdf · doi:10.48550/arxiv.2011.00278
published in arXiv (Cornell University) (Cornell University) · Submitted to IEEE EUSIPCO 2022
openalex publication_date 2020/10/31 · arxiv created 2022/02/19 · arxiv updated 2022/02/22 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
We present a compressive sensing based defect detection by multiple input\nmultiple output (MIMO) wireless radar. Here, defects are inside a layered\nmaterial structure, therefore, due to reflections from the surface of the\nlayered material structure the defect detection is challenging. By utilizing a\nlow-rank nature of the reflections of the layered material structure and sparse\nnature of the defects, we propose a method based on rank minimization and\nsparse recovery. To improve the accuracy in the recovery of low-rank and sparse\ncomponents, we propose a non-convex approach based on the iteratively\nreweighted nuclear norm and iteratively reweighted \ℓ1-norm algorithm. Our\nnumerical results show that the proposed method is able to demix and recover\nthe signalling responses of the defects and layered structure successfully from\nsubstantially reduced number of observations. Further, the proposed approach\noutperforms the state-of-the-art clutter reduction approaches\n