Multipath Ghost Suppression Based on Generative Adversarial Nets in Through-Wall Radar Imaging

Yong Guo,Gang Wang,Ruiyuan Song,Yong Jia,Xiaoling Zhong,Shengyi Chen,Guolong Cui

doi:10.3390/electronics8060626

Abstract

In this paper, we propose an approach that uses generative adversarial nets (GAN) to eliminate multipath ghosts with respect to through-wall radar imaging (TWRI). The applied GAN is composed of two adversarial networks, namely generator G and discriminator D. Generator G learns the spatial characteristics of an input radar image to construct a mapping from an input to output image with suppressed ghosts. Discriminator D evaluates the difference (namely, the residual multipath ghosts) between the output image and the ground-truth image without multipath ghosts. On the one hand, by training G, the image difference is gradually diminished. In other words, multipath ghosts are increasingly suppressed in the output image of G. On the other hand, D is trained to improve in evaluating the diminishing difference accompanied with multipath ghosts as much as possible. These two networks, G and D, fight with each other until G eliminates the multipath ghosts. The simulation results demonstrate that GAN can effectively eliminate multipath ghosts in TWRI. A comparison of different methods demonstrates the superiority of the proposed method, such as the exemption of prior wall information, no target images with degradation, and robustness for different scenes.

Highlights

For through-wall radar imaging (TWRI), the presence of furniture and walls, floors, and ceilings makes electromagnetic waves have strong reflections between the targets and them, which brings multipath returns to the received radar signal
In References [4,5], first-order multipath ghosts were mapped back to the positions of associated targets, while target images that overlapped with multipath ghosts were mistakenly removed from true positions
A generative adversarial nets (GAN)-based multipath-ghost suppression algorithm is presented in this paper

Summary

Introduction

For through-wall radar imaging (TWRI), the presence of furniture and walls, floors, and ceilings makes electromagnetic waves have strong reflections between the targets and them, which brings multipath returns to the received radar signal. Based on imaging algorithms, such as the back-projection algorithm [1,2,3], target-like images called multipath ghosts are produced at nontarget locations, which makes the performance of detection and recognition significantly worse. To solve this problem, a group of methods was designed via the multipath model based on prior information about the walls’ locations and antennas. To preserve the overlapped target images, multipath echoes were removed form the raw radar data in Reference [6]. In Reference [7], multiple estimated images gained by two different kinds of imaging dictionaries were fused to obtain an image without multipath ghosts

Methods

Discussion

Conclusion