Grating Lobe Suppression Based on FCN for Through-the-Wall 3D SAR Imaging

In through-the-wall 3D synthetic aperture radar (SAR) imaging, under-sampling in the height direction is usually unavoidable, resulting in severe grating lobe effects. Traditional grating lobe suppression algorithms have poor performance, and existing neural network research is limited to 2D SAR images, which cannot achieve high-quality grating lobe suppression for 3D SAR. To solve this problem, this paper proposes a grating lobe suppression algorithm for through-the-wall 3D SAR based on fully convolutional networks (FCN). Simulation results demonstrate that, compared to traditional grating lobe suppression algorithms, the proposed network significantly suppresses grating lobe effects in 3D SAR, achieving optimal imaging quality. Finally, the effectiveness of the algorithm is demonstrated with measured data.