论文标题
使用生成对抗网络的不均匀壁的电特性估计
Estimation of Electrical Characteristics of Inhomogeneous Walls Using Generative Adversarial Networks
论文作者
论文摘要
研究并开发了壁壁雷达，用于在室内环境中对人类活动的检测，定位和跟踪。通过墙壁传播电磁波在雷达特征中引入折射，衰减，多径和幽灵靶标。估计壁特性（介电轮廓和厚度）可以使壁效应从遍布雷达的特征进行反转。我们建议使用生成的对抗网络（GAN）来估计与发射器相同壁上窄带散射电场的壁特性。我们证明，由以对抗性方式配置的两个神经网络组成的gan能够通过有限的训练数据来解决高度非线性的回归问题，以估算介质概况和厚度，最高95 \％精度的实际壁的厚度，最高为95 \％的精度，该培训具有来自全波求解器的模拟数据。
Through-wall radars are researched and developed for the detection, localization, and tracking of human activities in indoor environments. Electromagnetic wave propagation through walls introduces refraction, attenuation, multipath, and ghost targets in the radar signatures. Estimation of wall characteristics (dielectric profile and thickness) can enable wall effects to be deconvolved from through-wall radar signatures. We propose to use generative adversarial networks (GAN) to estimate wall characteristics from narrowband scattered electric fields on the same side of the wall as the transmitter. We demonstrate that the GANs, consisting of two neural networks configured in an adversarial manner, are capable of solving the highly nonlinear regression problem with limited training data to estimate the dielectric profile and thickness of actual walls up to 95\% accuracy based on training with simulated data generated from full-wave solvers.
