Non-Line-Of-Sight Radar 3-D Imaging via Sparse Reconstruction

Non-line of sight (NLOS) radar imaging is significantly promising in several environmental perceptions, such as urban environment perception and intelligent driving. However, the existing NLOS imaging radar mainly focuses on two-dimensional (2-D) imaging, which may suffer from information loss and geometric distortion of the real three-dimensional (3-D) underlying scene. In this paper, exploiting the scene sparsity and the specular scattering of the reflector, we proposed an NLOS 3-D radar imaging method based on Bayesian compressed sensing sparse reconstruction and mirror projection. In the scheme, the imaging model of NLOS radar with the geometric mirror scattering is first constructed. And then, the sparse Bayesian learning method is used to perform high-resolution imaging of the NLOS objects. In order to verify the technique, we set up a millimeter-wave multiple-input multiple-output (MIMO) array scanning system, and different types of metal balls and irregular targets are tested. The experimental results demonstrate that the presented 3-D NLOS imaging radar can obtain the near-field high-resolution 3-D imaging of the tested targets, and the effectiveness of the sparsity-driven algorithm is verified.