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.

Through-wall imaging is capable of detecting various living and non-living things behind the wall. The characteristics of the wall under the investigation, amount of clutter and noise govern the quality and reliability of the image as well as the detection ability of the targets using through the wall imaging system. The characteristics of the wall are not known prior, in the literature only the intensity profile is investigated for the unknown wall characteristics using a single dielectric target and the effect of the wall characteristics on the contrast imaging and impact on time or frequency domain features are not investigated. The target with less dielectric is having less reflectivity; hence its detection in the presence of a high reflective target and a noisy environment becomes difficult. In this paper, to enhance the detection ability of the imaging system attenuation constant (α) of the wall is estimated with the proposed wall parameter estimation methods and used as a normalizing factor. To achieve effective beamforming different weighting strategies are developed and the obtained images are compared with the traditional beamforming. Furthermore, a novel approach to finding the effective rank in the low-rank estimation using a statistical model and multi-objective genetic algorithm is proposed for de-noising.