A robust and efficient wall parameter estimation approach for through wall radar

Abstract

Abstract In through-wall radar system, the wall parameters, including permittivity, and wall thickness are of crucial importance for locating targets precisely. Recently, to obtain a quick and accurate estimation of wall parameters, an approach based on machine learning was introduced. However, these approaches are less reliable as only simulations results are presented. One of the major concerns with machine learning-based approaches is the generation of training and testing data which require fabrication of wall with different permittivity, thickness, and conductivity. Creating walls with different permittivity, thickness, and conductivity can really be challenging and expensive. Therefore, an effort has been made in this paper to establish a cost-effective and robust machine learning-based wall parameter estimation process with the usage of transmission line method and artificial neural network. The implementation and efficacy of proposed approach have been demonstrated through simulation and experimental results. The proposed approach quickly and accurately predicted the wall relative permittivity and thickness of real building wall. The merit of proposed approach is that it is less complex and computational efficient as it can extract wall parameters from only one measurement and therefore can be used in conjunction with any commercial through-wall radar systems.