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In order to improve the speed of passenger screening while preserving the effective capability to detect more sophisticated threats, airport security imaging systems must be able to accurately characterize concealed body-worn objects. In addition to improving the passenger experience, this system capability will enhance airport security for the traveling public. This paper presents a real-time, fully-automatic algorithm for the wideband millimeter-wave (mm-wave) radar reconstruction of the nominal human body contours, even in the presence of an affixed weak dielectric object or when a portion of the body cross-section is not captured by the imaging scanner. The algorithm extracts the main contours from a noisy collection of 3D reconstructed reflectivity and approximates the nominal human body cross-section via fitting a low order angular Fourier series. This important step is essential for precise characterization of concealed body-worn explosives. A ranking algorithm is developed as a metric for the nominal body reconstruction accuracy. We verify the developed algorithm by applying it to the actual images of the High Definition-Advanced Imaging Technology (HD-AIT) system, a laboratory prototype mm-wave scanning system developed recently by the US Department of Homeland Security (DHS). The reconstructed body contours may be used to estimate the electric permittivity of the concealed person-worn objects.

Precise characterization of concealed person-worn objects will speed up the passenger screening process by reducing the rate of nuisance alarms, while also enhancing the airport security imaging systems. This paper presents an automatic, real-time method for wideband millimeter-wave radar identification of the nominal surface contours of the human body – even with affixed foreign objects or when a segment of the body cross-section is not captured by the radar – without relying on the body's bilateral symmetry. The developed method is verified experimentally when applied to the actual images generated by a laboratory airport scanning prototype developed recently by the US Department of Homeland Security (DHS). Our method uses the noisy collection of radar cross-section reflectivity data to extract the main contours and estimates the nominal body surface cross-sections through fitting a small-term Fourier series of circumferential variation. This is a necessary step for accurate characterizing of concealed terrorist threat objects affixed to the body.

The efficiency and effectiveness of airport security can be improved through fast and high-fidelity detection of concealed threat objects. If detected objects can be accurately characterized, the number of false alarms will be reduced, whereas terrorist threats can be more quickly noticed and apprehended. This article aims to accurately characterize the physical properties of weak dielectric objects (such as explosive threats) covering a conductive plane (such as the human body) using a single-frequency millimeter-wave (mm-wave) radar system with a Fresnel aperture. While the conventional synthetic aperture radar (SAR) method requires data over a broad bandwidth, our proposed method works even at a single frequency and is particularly suitable for focused-beam CW mm-wave sensing systems. This method is validated with experimental data.

A major problem with forward-looking ground-penetrating radar (FLGPR) detection of buried explosive threats is the scattering from the rough ground surface. The authors have previously developed a real-time 3-D algorithm for single-view emulation of synthetic aperture FLGPR scattering from rough terrain at low grazing angles. This article extends the method to the real-time 3-D simulation of a multiview moving platform transmitter/receiver array moving as fast as 15 km/h. The need to perform the new calculation at every frame for the moving antenna array platform is shown to be unnecessary, with the requirement relaxed to once for only a single frame at the center of 10–20 m forward interrogation range, leading to about 50 times faster computation. The computation of the scattered waves comprising surface clutter is reduced for all moving frames to a mere multiplication of three matrices: a pre-computed impulse response matrix of rough terrain, a pre-computed correction matrix of moving frames, and a matrix characterizing the transmitting input signal. The method is evaluated via 3-D Monte Carlo simulation for various rough surface parameters, and its applicability for subsurface scattering reconstruction to characterize the buried threat objects is shown. For a vehicle-mounted FLGPR detection system, this leads to a significant saving of computation resources. Our developed algorithm provides 3-D modeling of rough terrain scattering for lossy and frequency-dispersive soil and compares well with full-wave finite-difference frequency-domain (FDFD) method computation.