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Fast detection of hazardous objects in millimeter-wave personnel screening can increase the operating efficiency of secure environments. In airports, accurate automatic detection and classification of body-worn objects can reduce the number of pat-downs while keeping the transportation environment safe and secure. Since many benign objects, such as paper and leather, are lossy materials, being able to characterize them and set them aside from the secondary checks will significantly reduce the screening process time. In this article, we introduce an effective image-processing-based method for characterizing lossy materials that might be concealed under clothing at checkpoints. The method has been combined with a modified version of the previously developed algorithm that can be used specifically to characterize lossless materials. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross section of the body, we extract the nominal body contour using the Fourier series, separate body, and object responses, categorize the object as lossy or lossless based on the depression and protrusion of the body contour, and, finally, predict possible values for the object’s permittivity and thickness. Our resulting classification is good, implying fewer nuisance alarms at checkpoints. [ABSTRACT FROM AUTHOR]

A new method extending the scope of the superalgebraically convergent 2-D wave diffraction algorithms is proposed. The principal tool is the construction of an infinitely smooth approximation to the boundary contour. The construction is based on the ideas of Tikhonov’s regularization to the stable summation of the Fourier series and the evaluation of unbounded operators. Both the algorithms for smoothing and the nonsaturated solutions for integral equations together provide the superalgebraic (faster than any algebraic) convergence. The comparison of the algorithms using the original (noninfinitely smooth) parameterization and the infinitely smooth one demonstrates the essential advantage of the latter over the former with respect to accuracy and time consumption. [ABSTRACT FROM AUTHOR]

This work continues the development of the ray-tracing method of de Jong (2021) for computing the scattered fields from metasurfaces characterized by locally periodic reflection and transmission coefficients. In this work, instead of describing the metasurface in terms of scattering coefficients that depend on the incidence direction, its scattering behavior is characterized by the surface susceptibility tensors that appear in the generalized sheet transition conditions (GSTCs). As the latter quantities are constitutive parameters, they do not depend on the incident field and, thus, enable a more compact and physically motivated description of the surface. The locally periodic susceptibility profile is expanded into a Fourier series subject to a spatially varying phase parameter, and the GSTCs are rewritten in a form that enables them to be numerically solved for the reflected and transmitted surface fields. The phase parameter can either be determined from a prescribed surface transformation or extracted from known surface susceptibilities. A method for the extraction of this phase parameter from a susceptibility profile using a spatial variant of the short-time Fourier transform (STFT) is proposed. The scattered field at arbitrary detector locations is constructed by evaluating critical-point contributions of the first and second kinds using a forward ray-tracing (FRT) scheme. The accuracy of the resulting framework has been verified with an integral equation-based boundary element method (BEM)-GSTC full-wave solver for a variety of examples, such as a periodically modulated metasurface, a metasurface diffuser, and a beam collimator. [ABSTRACT FROM AUTHOR]