Three-dimensional (3-D) through-the-wall imaging is a challenging topic. It has attracted some research attention in recent years. The 3-D structure is hard to reconstruct because of the limited measurement in the CT-mode imaging method. In order to obtain an accurate 3-D result, the 3-D total variation (3-D TV) algorithm has been adopted. However, the result suffers from image blurring and artifacts. In this paper, a tensor-based optimization framework is proposed to exploit more features of the 3-D wall structure and make up for the shortcomings of the 3-D TV algorithm. The 3-D building structure is modeled as a three-order tensor. Just like the 3-D TV algorithm, the local similarity is considered by the TV regularization constraint to guarantee the reconstruction of the edge. Besides, the group sparsity of the structure is considered to suppress the effect of artifacts and blur. Moreover, in order to keep the global correlation of the image in the case of the errors, the tensor Tucker decomposition is adopted. The performance of this method is discussed in the simulation and real radar data results. It shows that the artifacts and blur are suppressed effectively and the 3-D structure is kept as well.

Array-InSAR tomography has become a field of intensive research to reconstruct urban 3D point clouds. However, multipath interference is very obvious in the reconstructed 3D point clouds, seriously affecting the recognition, extraction, and modeling of the building. In this letter, the authors proposed a novel multipath suppression method to improve the quality of point clouds, which mainly utilizes the characteristics of secondary and tertiary scattering. The performance of the method is verified through airborne array-InSAR data. Experimental results show that the proposed method can automatically obtain large scene urban 3D point clouds without multipath interference.