Using the Convolutional Neuron Network for Target Localization and Wall Characterization in the Through the Wall Imaging Problem

This paper investigates the Through the Wall Imaging (TWI) problem via machine learning. To solve the TWI problem in presence of the wall, a Convolution Neuron Network (CNN) is proposed. The detection of the scatterers behind the wall when there is a strong reflection from the front wall is very challenging. Several microwave imaging algorithms have been introduced to extract the unknown parameters of the wall and mitigate the wall clutter to predict the location of the target. This process is very time-consuming even though it has accurate results. In this paper, we developed a method for exploiting the complex information for the TWI problem by using a CNN that accepts complex numbers from the receiver to predict wall thickness, material, and location of the target behind the wall at the same time. We show that our proposed model can predict these parameters with an accuracy of 92.6%.