A novel technique for wall parameter estimation for through wall imaging systems

Abstract

AbstractAccurate estimating of wall parameters is crucial for precisely locating targets and obtaining well‐focused through wall radar images. Recently, machine learning has been introduced for quick and precise estimation of wall parameters. One of the significant concerns with this approach is the generation of training and testing data, which require the fabrication of walls with different permittivity, thickness, and conductivity. Creating walls with varying permittivity, thickness, and conductivity can be difficult and expensive. Therefore, a cost‐effective method for estimating wall parameters is proposed in this paper. The proposed method uses an electromagnetic approach to train the artificial neural network to retrieve the relative permittivity and thickness of the wall. The proposed estimation approach has the advantages of high precision and low computational complexity. The performance of the proposed approach is validated on simulated and experimental data. The applicability of the proposed method has been shown with measured data from two real building walls. It is shown that the proposed method retrieved wall parameters with high accuracy.