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Carey M. Rappaport

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Prof. Carey M. Rappaport
Dept. Electrical and Computer Engineering
Northeastern University, Boston, MA 02115, USA
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Prof. Carey M. Rappaport is a Fellow of the IEEE, and he received five degrees from the Massachusetts Institute of Technology (MIT): the S.B. degree in mathematics and the S.B., S.M., and E.E. degrees in electrical engineering in 1982, and the Ph.D. degree in electrical engineering in 1987. Prof. Rappaport has worked as a teaching and research assistant at MIT from 1981 to 1987 and during the summers at Communications Satellite Corporation Laboratories in Clarksburg, Maryland, and The Aerospace Corporation in El Segundo, California. He joined the faculty at Northeastern University in Boston, Massachusetts, in 1987 and has been a professor of electrical and computer engineering since July 2000. In 2011, he was appointed College of Engineering Distinguished Professor. During the fall in 1995, he was a visiting professor of electrical engineering at the Electromagnetics Institute of the Technical University of Denmark, Lyngby, as part of the W. Fulbright International Scholar Program. During the second half of 2005, he was a visiting research scientist at the Commonwealth Scientific Industrial and Research Organization in Epping, Australia.

He has consulted for CACI; Alion Science and Technology, Inc.; GeoCenters, Inc.; PPG, Inc.; and several municipalities on wave propagation and modeling, and microwave heating and safety. He was a principal investigator for the Army Research Office-sponsored Multidisciplinary University Research Initiative on Humanitarian Demining, a coprincipal investigator for the National Science Foundation-sponsored Engineering Research Center for Subsurface Sensing and Imaging Systems, and a coprincipal investigator and deputy director for the Department of Homeland Security-sponsored Awareness and Localization of Explosive Related Threats Center of Excellence.

Prof. Rappaport has authored more than 400 technical journal articles and conference papers in the areas of microwave antenna design, electromagnetic wave propagation and scattering computation, and bioelectromagnetics, and he has received two reflector antenna patents, two biomedical device patents, and four subsurface sensing device patents. As a student, he was awarded the AP-S’s H.A. Wheeler Award for best applications paper in 1986. He is a member of the Sigma Xi and Eta Kappa Nu professional honorary societies.

Localizing tunnel positions under rough surfaces with underground focused synthetic aperture radar

Underground tunnels present both military and homeland security threats since smugglers use them as transit routes for trafficking weapons, explosives, people, drugs, and other illicit materials. Detecting and imaging the presence of tunnels in any given region of ground is possible because the air that fills them is materially quite different from anything else underground. Spotlight Synthetic Aperture Radar (SL-SAR) has been used to detect tunnels due to its ability to scan large areas of terrain in a short amount of time. In order to obtain strong and distinct target signals, underground focusing, based on ray refraction at the ground surface must be considered. This presents a challenge since the technique requires an estimation of the ground characteristics, and the random roughness of the soil surface tends to distort the reconstructed image of the analyzed geometry.

This presentation explores the impact of the surface roughness in underground focusing SAR imaging for tunnel detection applications. The study starts by simulating incident plane waves from 19 angles (-45 to 45 degrees) at 128 different frequencies (55 to 550 MHz) with 2-D Finite Difference Frequency Domain (FDFD) analysis on 2 different types of soil: non-dispersive sandy soil and lossy clay loam soil with 10 cm of randomly distributed roughness. It is demonstrated that a shallow tunnel can be imaged in low moisture, non-conductive sand, but that the more lossy moist clay presents too much surface clutter to distinguish the tunnel.

Modeling Standoff Radar Scattering of Concealed Body-Worn Objects for Suicide Bomber Detection

The problem of detecting hazardous concealed targets at a safe distance is of tremendous importance. Suicide bombers pose an increasingly critical terrorist threat. Because of their free mobility and ease of concealment, it is challenging to protect people in crowded areas from harm. Although there are many ways to detect the presence of explosives on individuals with intimately close sensors, radar is one of the very few means of sensing dangerous objects obscured by clothing at 50 m.

Radar is conventionally used to detect metallic objects in air or image features on relatively flat backgrounds. For body-worn explosives detection, the explosive material and the metal casing that generates shrapnel is often difficult to distinguish with radar, since the body is electrically large, non-ideal in shape, and quite reflective. Skin and muscle tissue and blood are high in water content, so the planar reflection coefficient for microwaves and millimeters waves is about 0.7. Even innocent individuals scatter strongly. In addition, since narrow illumination beams require electrically large radar antenna apertures, millimeter waves are preferred, making the body a huge scatterer with considerable interference scattering from its various parts.

This talk discusses finite difference frequency domain (FDFD) computational modeling of radar wave propagation and scattering from metallic pipes on an anatomically-derived torso cross-section model. The nearfield wave interaction shows explicitly why simple threshold detection of return signal is insufficient for detecting the presence of pipes on the body. It also indicates how narrowing the transmitted beam can reduce the clutter from the body, and make pipe detection feasible at standoff distances.

Multistatic 3D Whole Body Millimeter-Wave Imaging for Explosives Detection

A whole-body imaging system for concealed object detection using multistatic mm-wave radar is presented. Horizontal multistatic sensing is facilitated using a patented “blade beam” transmitting reflector antenna and a quarter-circle arc array of receivers. The blade beam reflector combines parabolic curvature in the horizontal plane with elliptical curvature in the vertical plane to focus to a narrow horizontal slice on the object to be imaged. With only this slice illuminated, the scattered field will be due to just this narrow portion of the object, allowing for computationally simple inversion of a onedimensional contour rather than an entire two-dimensional surface. Stacking the reconstructed contours for various horizontal positions provides the full object image. 3D high resolution images are generated using a two-step process. Initially, an inverse source-based Fast Multipole Method (iFMM) provides a first approximation to the true human torso. Afterwards, the retrieved geometry is refined using the Iterative Field Matrix (IFM) technique. Assuming smooth variations of the human body profile, the object detection is performed by comparing the retrieved surface with a smoothed one. Results are based on Physical Optics simulations of the human body, considering both cases with and without objects.

Electromagnetic Sensing and Treatment of Living Things: Using Microwaves to Detect and Treat Disease in Humans and Trees

Because of their ability to penetrate and heat, electromagnetic waves have found use in several unusual applications, specifically in interaction with biological tissue. Microwave radar has been used as an anatomic imaging modality for detecting breast cancer, and THz radiation is being proposed for vulnerable plaque identification. Using a simple conformal antenna, microwave sensing of trees can alert arborists if there is an otherwise undetectable infestation of Asian Long-Horned beetle. By depositing microwave power at depth, cancerous or otherwise diseased tissue can be non-invasively heated and inactivated or ablated while sparing healthy surrounding tissue. This survey presentation will touch on a variety of life science electromagnetic applications, discussing feasibility, advantages, efficacy, and limitations of the proposed approaches.

Advanced Concepts for Ground Penetrating Radar Detection of Land Mines

Underground tunnels present both military and homeland security threats since smugglers use them as transit routes in order to avoid security checkpoints and to traffic people, weapons, drugs, and other illicit materials. In addition, assailants might use tunnels to burrow under high security facilities and detonate lethal explosives. Tunnel detection and real-time monitoring will effectively improve border security and diminish potential underground hazards.

Detecting and imaging the presence of illicit tunnels in any given volume of soil is possible because the air that fills them is materially quite different from anything else underground. The Spotlight Synthetic Aperture Radar (SL-SAR) concept has been suggested for tunnel detection due to its ability to scan large areas of terrain in a short amount of time, which is ideal for tunnel detection. We make use of Underground Focusing (UF) SL-SAR, in which a phase correction based on ray refraction at the ground surface was introduced for more accurate focusing. This project simulated incident electromagnetic plane waves with 19 different angles and 128 different frequencies (50 MHz – 550 MHz) using 2-D Finite Difference Frequency Domain analysis to reconstruct the location of subsurface tunnels in sandy soil and clay loam soil. By measuring the electric field distribution along 19 receiving antennas located in the farfield, 100 m. above the surface, the focusing algorithm can create the reconstruction images. It is shown that shallow tunnel detection with airborne techniques on low-loss non-dispersive sandy soil may be possible. Tunnel detection is a difficult problem that depends on many different variables such as soil constitutive parameters and antenna configuration. In addition, this work analyzes the parametric impact of different tunnel depths, roughness topologies, and roughness heights in the UF-SL-SAR images.