使用深度神经网络同时估计TWR中的壁和对象参数
Simultaneous estimation of wall and object parameters in TWR using deep neural network
摘要：
本文提出了一种在穿墙雷达中同时估计目标和墙壁参数的深度学习模型。在这项工作中，我们考虑两种模式：单目标和两个目标。在这两种情况下，我们考虑的电容率和壁的厚度，以及目标的中心和电容率的二维坐标。这意味着在单个目标的情况下，我们估计五个值，而在两个目标的情况下，我们同时估计八个值，每个值代表上述参数。我们发现，当使用深度神经网络解决目标定位问题时，为模型提供更多的问题参数可以提高定位精度。结果表明，在问题中加入两个壁面参数，当壁面参数被估计时，目标定位的精度得到了提高。通过使用深度神经网络模型，我们能够以99%的精度估计壁介电常数和厚度的参数，以及单目标和双目标模式下目标的二维坐标和介电常数。
英文摘要：
This paper presents a deep learning model for simultaneously estimating target and wall parameters in Through-the-Wall Radar. In this work, we consider two modes: single-target and two-targets. In both cases, we consider the permittivity and thickness for the wall, as well as the two-dimensional coordinates of the target's center and permittivity. This means that in the case of a single target, we estimate five values, whereas, in the case of two targets, we estimate eight values simultaneously, each of which represents the mentioned parameters. We discovered that when using deep neural networks to solve the target locating problem, giving the model more parameters of the problem increases the location accuracy. As a result, we included two wall parameters in the problem and discovered that the accuracy of target locating improves while the wall parameters are estimated. We were able to estimate the parameters of wall permittivity and thickness, as well as two-dimensional coordinates and permittivity of targets in single-target and two-target modes with 99\% accuracy by using a deep neural network model.
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