Multipath Ghost and Side/Grating Lobe Suppression Based on Stacked Generative Adversarial Nets in MIMO Through-Wall Radar Imaging

Abstract

For multi-input multi-output (MIMO) through-wall radar imaging (TWRI), multipath ghosts and side/grating lobe artifacts degrade the imaging quality of the obscured targets inside an enclosed building, therein hindering target detection. In this paper, an approach based on two stacked generative adversarial nets (GAN) is proposed to achieve multipath and side/grating lobe suppression with regard to MIMO TWRI. Specifically, the Stage-I GAN constructs a spatial structure mapping from the original input images to the Stage-I GAN output images with the suppressed multipath ghosts. However, the side/grating lobe artifacts are intentionally preserved in the stage-I GAN as additional constraint information to prevent uncontrollable over-fitting. Then, the Stage-II GAN takes the output images of Stage-I GAN as input to suppress the side/grating lobe artifacts. Extensive electromagnetic simulations and comparisons demonstrate that the proposed approach achieves better suppression of multipath ghosts and side/grating lobe artifacts and other significant superiorities, including priori wall information not being required, the preservation of weak targets, and robustness for different array deployments and building layouts.