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

TL;DR

This paper presents a deep learning method for accurate two-dimensional multi-target locating through complex electromagnetic walls in radar systems, demonstrating high accuracy even under noisy conditions.

Contribution

It introduces a deep neural network approach for through-the-wall radar target localization considering complex wall scenarios, with a constructed dataset and high accuracy results.

Findings

97.7% accuracy for single-target locating

94.1% accuracy for double-target locating

10-20% accuracy loss at low SNRs

Abstract

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.