  This paper employed deep learning to do two-dimensional, multi-target
locating in Through-the-Wall Radar under conditions where the wall is treated
as a complex electromagnetic medium. We made five assumptions about the wall
and two about the number of targets. There are two target modes available:
single target and double targets. The wall scenarios include a homogeneous
wall, a wall with an air gap, an inhomogeneous wall, an anisotropic wall, and
an inhomogeneous-anisotropic wall. Target locating is accomplished through the
use of a deep neural network technique. We constructed a dataset using the
Python FDTD module and then modeled it using deep learning. Assuming the wall
is a complex electromagnetic medium, we achieved 97.7% accuracy for
single-target 2D locating and 94.1% accuracy for two-target locating.
Additionally, we noticed a loss of 10% to 20% inaccuracy when noise was added
at low SNRs, although this decrease dropped to less than 10% at high SNRs.
