Target Imaging Based on Generative Adversarial Nets in Through-wall Radar Imaging

Abstract

For multi-input multi-output (MIMO) through-wall radar imaging (TWRI), multipath ghosts, side/grating lobe artifacts and wall penetration effect degrade the imaging quality of the obscured targets inside an enclosed building, there in hindering target detection. In this paper, an approach based on generative adversarial nets (GAN) is proposed to achieve multipath ghosts, side/grating lobe artifacts and wall penetration effect suppression with regard to MIMO TWRI. Specifically, the whole task is divided into two steps (Firstly, multipath ghosts and wall penetration effect are suppressed but the side/grating lobes are preserved. Secondly, side/grating lobes are eliminated.) Then a GAN network is applied to solve those two steps. Extensive electromagnetic simulations and comparisons demonstrate that the proposed approach achieves better suppression of multipath ghosts, side/grating lobe artifacts, wall penetration effect and other significant superiorities, including priori wall information not being required and robustness for different array deployments and building layouts.