3-D Imaging Under Low-Rank Constraint With Radio Signals

Compared with optical imaging, radio frequency (RF) imaging enables object imaging in an all-weather, privacy-preserving, and cost-effective way. However, the existing heuristic solutions such as back projection (BP) and range migration (RM), 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 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.