3D Radio Imaging Under Low-Rank Constraint

Radio frequency (RF) imaging has been of interest for years due to its ability to create images of targets without being affected by weather and light conditions. The existing heuristic solutions such as back projection (BP), suffer from the sidelobe interference due to limited antenna aperture. In this paper, we propose a super-resolution 3-D imaging algorithm to enhance imaging performance. Mathematically, the reconstruction problem is an inverse problem that can be formulated as an optimization problem. The low-rank property of the object is exploited to regularize the imaging problem by nuclear norm minimization. We also develop a coarse-to-fine scan structure to reduce the computational complexity of the proposed algorithm. We evaluate the proposed algorithm with both simulations and measurements. With a frequency band range from 2. 7-4. 1 GHz and a 16 6 multiple-input-multiple-output (MIMO) antenna array, simulation results show high imaging quality with a median boundary keypoint precision of 2 cm, and experimental results validate the feasibility of the proposed algorithm in a real-world environment.