Deep Learning Approach for Target Locating in Through-the-Wall Radar under Electromagnetic Complex Wall

In this paper, we used the deep learning approach to perform two-dimensional, multi-target locating in Throughthe-Wall Radar under conditions where the wall is modeled as a complex electromagnetic media. We have assumed 5 models for the wall and 3 modes for the number of targets. The target modes are single, double and triple. The wall scenarios are homogeneous wall, wall with airgap, inhomogeneous wall, anisotropic wall and inhomogeneous-anisotropic wall. For this purpose, we have used the deep neural network algorithm. Using the Python FDTD library, we generated a dataset, and then modeled it with deep learning. Assuming the wall as a complex electromagnetic media, we achieved 97:7% accuracy for single-target 2D locating, and for two-targets, three-targets we achieved an accuracy of 94:1% and 62:2%, respectively.