A Novel Method for 3-D Building Structure Determination in
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.