FDFD Simulations

This paper presents a deep learning model for fast and accurate radar detection and pixel-level localization of large concealed metallic weapons on pedestrians walking along a sidewalk. The considered radar is stationary, with a multi-beam antenna operating at 30 GHz with 6 GHz bandwidth. A large modeled data set has been generated by running 2155 2D-FDFD simulations of torso cross sections of persons walking toward the radar in various scenarios. 

本论文提出一款深度学习模型，可对沿人行道行走的行人所携带的大型隐蔽金属武器实现快速精准的雷达检测与像素级定位。本次研究采用的雷达为固定式设备，搭载多波束天线，工作于30 GHz频段，带宽为6 GHz。研究团队通过开展2155次针对不同场景下朝向雷达行走的行人躯干横截面的二维频域有限差分（2D-FDFD）仿真，构建了大规模建模数据集。