Through-the-Wall Radar under Electromagnetic Complex Wall: A Deep Learning Approach
This addresses target detection for radar applications in challenging wall environments, but it is incremental as it applies existing deep learning methods to new wall scenarios.
The paper tackled 2D multi-target locating in Through-the-Wall Radar for complex electromagnetic walls using deep learning, achieving 97.7% accuracy for single targets and 94.1% for two targets.
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.