2.5D Quantitative Millimeter Wave Imaging of a Hidden Object on a Simplified Human Body Model Using Value Picking Regularization

Abstract

In this contribution, the authors provide a proof of principle for quantitative imaging of concealed objects on the human body using millimeter waves. A two-and-a-half-dimensional (2.5D) quantitative millimeter wave imaging algorithm is applied to reconstruct a hidden dielectric object on a clothed simplified human body model. At millimeter wave frequencies, the incident field is typically a fully three-dimensional (3D) Gaussian beam, illuminating only a limited spot on the body. Due to the large dimensions of the human body in terms of wavelengths, a 3D discretization is hardly feasible. Therefore, it is assumed that the electromagnetic properties of the body do not significantly change within the illuminated spot, along the longitudinal direction of a person. Hence, only the cross-section of a human body model is discretized. This 2.5D assumption however is still not sufficient to reduce the forward problem to a feasible size. Therefore, a priori knowledge on the illumination and on the scattering properties of the clothed human body is used to deduce a simplified model to describe the cross-section of the clothed human abdomen. The complex permittivity profile of a small dielectric object, hidden underneath clothing and representing some type of explosive, is reconstructed. The complex permittivity profiles of all other scatterers are assumed to be known. The presented quantitative inverse scattering algorithm is based on a Newton-type optimization, combined with an approximate line search and regularized by applying Stepwise Relaxed Value Picking regularization. The input data of the quantitative inverse scattering problem are synthetic scattering data since the authors are not aware of any amplitude and phase measurement data for concealed weapon detection yet made available to the inversion community at these high frequencies.