# Through-the-Wall Radar under Electromagnetic Complex Wall: A Deep Learning Approach

arXiv: https://arxiv.org/abs/2102.07990 · HTML: https://mlsanity.com/paper/2102.07990/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2021.

## Built on by (most cited first, 1 of 1)

- [Deep Learning Framework for the Design of Orbital Angular Momentum Generators Enabled by Leaky-wave Holograms](https://mlsanity.com/paper/2304.12695/) (2023)

## Abstract (arXiv)

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.