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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.

We develop a model for a dielectric-coated perfectly conducting cylinder, as imaged by a wideband millimeter-wave radar security scanning system. This model images concealed dielectric objects, when attached to realistically curved body parts, including the human arm or leg. The model is compared to that of a planar dielectric slab on a perfectly conducting plane, and it is proven to be nearly identical for dielectrics with thickness of less than half the cylinder radius.

Microwave nearfield imaging is useful for security applications as it safe, non-invasive and penetrates clothing. The phase details of the radar measurements are essential for reconstructing images of metal or dielectric objects which may be hidden on the body. In this paper, we model the body as a flat perfect electrical conductor (PEC) 20 cm in radius. The threat is a cylindrical explosive "puck" 50 mm in radius and 37 mm thick with the dielectric properties of TNT. The body-with-target is probed by 31 millimeter-wave wideband (10-40 GHz) transmitting radar units, each modeled as a right-circularly polarized (RCP) point source, which all lie on an arc in front of the body in the plane of Figure 1 . Thirty-one point source receivers are co-located with the transmitters. The 2½ Dimensional Quasi-Axisymmetric Finite Difference Frequency Domain (2.5D QAFDFD) computational code [1] , which solves the full 3D Helmholtz equation for arbitrary sources in axisymmetric geometries, is used to determine the cross-polarized received fields everywhere in space and particularly in the plane of the arc formed by the sources. This full wave computational tool can accurately model the corner, edge, diffraction and dielectric effects which are not fully captured by ray-based or physical optics methods.

Ground penetrating radar (GPR) is a viable tool for fast and high fidelity detection of concealed explosive threats. The radar effectiveness is limited by scattering from rough terrain which considerably obscures the buried target response. To calculate the rough ground scattering, a 3-D full-wave algorithm such as finite-difference frequency-domain (FDFD) method is required but is often prohibitive for multiple frames when the GPR antennas are distant from the target region. This paper presents a real-time 3-D modeling of a moving platform forward-looking GPR scattering from rough terrain located at great electrical distances from the GPR antenna. For a synthetic aperture, the computational domain of the focal region is reduced to a very small subset of the entire observed volume, and the surface clutter is computed via a mere multiplication of a precomputed impulse response matrix of the rough ground with the matrix characterizing the GPR transmitting signal. For a vehicle-mounted GPR detection system, this results in a significant reduction of complexity and saving of computation resources. The effectiveness of the algorithm is evaluated through an implementation of 3-D Monte Carlo simulation for various rough surface parameters. Our developed model compares well with the direct FDFD results, and can be used for lossy and frequency-dispersive soils.

This paper describes the use of pre-trained model classifiers in detecting epileptic seizures. A patient specific classifier was trained using features extracted from the intracranial electroencephalogram (iEEG) signals. Both accuracy and the loss function are used for performance evaluation.

In this paper we study the use smart glasses in classifying simulated epileptic seizure signals. We train a patient specific classifier using features extracted from an inertial measurement unit signals. For performance evaluation, we use the accuracy as well as the loss function values and Root-Mean-Square Error (RMSE). Long short-term memory (LSTM) neural network is used on the data collected from the smart glasses. Orhan et al. (Exp Syst Appl 38:13475–13481, 2011), Samiee et al. (IEEE Trans Biomed Eng 62:541–552, 2015)

This chapter constitutes a first step toward a wearable epileptic seizure prediction device. Since recording electrocardiogram can be accomplished fairly easily, we look into the existing correlation between epileptic pre-ictal states and heart rate variability. The intervals of extreme noise may corrupt the electrocardiogram data during the seizures, and this means that we are able to use a machine learning and specifically deep learning techniques to detect the pre-ictal aka pre-seizure states. The experimental results show particularly good results in terms of prediction performance. They also show the importance of a specific training for each patient. In this chapter, we analyzed the cardiac dynamics in patients with partial epilepsy. By doing so, we discovered transient but prominent low-frequency heart rate oscillations immediately following seizures in some patients. These features have been used for understanding cardiac and neuro-autonomic instability in epilepsy and also for classifications of such heart rates.

This paper proposes a method for determining permittivity and thickness of body-borne objects automatically by processing wideband radar images. The algorithm can be used to find the explosive threats and rule out the benign objects. Having the reconstructed millimeter wave radar image of the body with an anomaly attached to it, we extract the nominal body contour, which shows the body surface in the absence of the object, then we subtract the ideal body response from the image and define the amount of body displacement observed in the radar image which is caused by the signal retardation due to presence of the weak dielectric object and look for the front surface reflection of the attached foreign object. Finally, we calculate the amount of permittivity based on body displacement and the anomaly’s thickness.

Accurate characterization of suspicious body-worn objects may speed up the passenger screening process by reducing the number of manual screenings, while also maintaining the ability to detect more complex threats. This improves passenger experience in the screening process while preserving strong security. This paper presents an innovative real-time method for millimeter wave nearfield radar reconstructing the nominal contours of the human bodies without affixed foreign objects. This important step is required for characterizing unique aspects of concealed objects when they are affixed to the body. The method is verified experimentally when applied to the actual images of a recently developed laboratory prototype millimeter-wave scanning system. We show that the method works well both when there is a weak dielectric object affixed to a human body and when a portion of the body cross-section is not captured by the imaging system. The reconstructed contour can be used to estimate the dielectric constant of the suspicious body-worn objects.

In this work, we investigate the application of the phase coherence method for improving the quality of reconstructed images of small isolated objects with our Advanced Imaging Technique (AIT) nearfield millimeter-wave radar security scanning system. Based on the phase diversity of the reconstructed solutions for different transmitters, a phase coherence factor (PCF) is designed to weight the coherent sum. We verify its effectiveness with both numerical simulation and experimental measurement. In both simulation and experiment results, the artifacts like side-lobes, grating lobes or clutter in the original images are reduced in the processed images after applying the phase coherence method.

This paper presents a new method for automatically determining the dielectric permittivity and thickness of a penetrable dielectric affixed to the human body by processing radar image responses. This is an important problem for explosives detection with body security scanners, with the potential for reducing the false alarm rate. Starting with reconstructed multistatic mm-wave images of a conductive surface partially covered with a weak dielectric bar, the algorithm determines the nominal conductive surface, identifies the position of the surface-attached dielectric anomaly, finds the front and back dielectric surface reflection responses, and then determines the permittivity and thickness of the dielectric bar.

Predictive 3D mm-wave nearfield radar modeling requires challenging tradeoffs between computational size and accuracy. For raster-scanned focused spot antenna systems, both the focusing array antenna and the target region must be finely and fully sampled, but the intervening space is large, making the computational space huge. For focusing along the axis in target geometries with only material variation in range, it is possible to scale the transmitter/receiver array and its range to target by a factor of two or four. But for even small amounts of off-axis focusing, the scaling must be done with care to avoid specular reflected rays that might miss the scaled transceiver array. Simulations based on ray analysis, analytic arrays of dipoles, and Quasi-Axisymmetric Finite Difference Frequency Domain (QAFDFD) compare well with each other. Only QAFDFD, however, can model target edge effects in the presence of large ground planes.

Building on a previously developed virtual-source model for loss-less dielectric slabs attached to metallic planes, we present a fast focused raster scanned spot continuous-wave (CW) method to find the thickness, dielectric permittivity, and loss-tangent of lossy dielectric slabs that could potentially be explosive threats. A library of reflection responses for objects with different sizes and electric properties is precomputed, and an inversion algorithm incorporates the measured data to predict the object features. It is shown that this characterization is possible even using only a single working frequency. The method is validated empirically by comparing to the synthetic reflection of the millimeter-wave imaging radar.

Millimeter-wave imaging radar is becoming increasingly popular for sensing and security applications. Usually full phased arrays, synthetic apertures, or focusing reflect-arrays are employed to sense a given image space. One new approach is to generate an axially-scanning focal spot using an axial nearfield bifocal dual reflector. The bifocal has two perfect foci on the symmetry axis, each corresponding to the two perfect feed points on the axis, made possible when two degrees of freedom – two reflectors – are employed. With two perfect foci, the optical aberrations for other image points between them must be of even order, which leads to a reasonable focal region bounded by the perfect foci. It is envisioned that a small feed moving along the axis might interrogate the image region, with better precision and speed, and less force than the full focusing stage. Alternatively, an electronically selective line source could scan the image space at nanosecond rates using only one dimension of sensing hardware. The concept is referred to as a microscope, because it maps rays diverging from the image space to rays converging at the observation points much like an optical microscope.

IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (2019. Atlanta), In this contribution, a novel Ground Penetrating Radar (GPR) configuration to detect low-contrast buried targets is presented. It is based on combining the advantages of Forward-Looking GPR (FLGPR) and Down-Looking GPR (DLGPR) using a distributed GPR system, with a vehicle-mounted transmitter looking ahead and a drone-mounted receiver looking downwards. This configuration provides good penetration and high resolution, reducing the clutter compared to DLGPR. Several simulations have been performed and the resulting Synthetic Aperture Radar (SAR) images have been compared., This work has been partially supported by Government of Spain (project TEC2014-55290-JIN, and grants FPU15/06341 and EST17/0777) and Government of Asturias (project GRUPIN-18-000191).

This paper details a model for a weak dielectric object on the human body with a small gap between the dielectric and body, as detected by a CW millimeter-wave security scanning system. This model is a refinement of the Virtual Source Model, focusing on the possibility that the dielectric is not directly against the skin. Seven primary scattering phenomena are considered in this model, more than in the Virtual Source model; later phenomena have negligible amplitude. Even a small gap can cause a significant difference in the return.

We propose a fast and accurate method of finding the thickness and dielectric permittivity of weak dielectric objects using a focused continuous-wave radar security scanning system, to characterize objects that may be explosive threats. Building on the previously developed ray-based Virtual Source model for sensing scattering from slabs, this work presents an inversion algorithm using two observable CW millimeter wave probe parameters by considering expected small variations of dielectric slab thickness over its surface. The determination works reasonably well with a simulated dielectric covered conductive ground plane as a representation of body worn explosives.

We develop a Virtual Source Model which is an intuitive, non-iterative, forward model to analyze the differential scattering of a dielectric covered conductive ground plane. This simple model is well suited for focused beam, nearfield mm-wave sensing of body-worn explosives for person scanning security systems. Ray analysis using the method of images for multiple interfaces yields good, experimentally-validated results.

Millimeter-wave imaging systems have been successfully used to detect security threats in airport checkpoints. Extracting the exact contour of the object under test from the synthetic aperture radar (SAR) image is important in order to enhance the probability of threat detection of the imaging system. Unfortunately, extracting accurate contours from the SAR image is a challenging task. The latter drawback is due to blurring effect introduced by the point spread function (PSF) of the system in the SAR image. In this letter, a regularization method that promotes smooth, sparsity-driven solutions of the imaging equation is used to improve the contour extraction of the object under test. Preliminary results show that the extracted contour of the proposed approach has a root mean square (RMS) error that is 28%–35% smaller than that of the traditional, nonregularized approach.

An extension of compressed sensing (CS)-based millimeter-wave imaging techniques from two- to three-dimensional (2-D to 3-D) is presented. The idea is to study the reduction in the minimum number of receivers with respect to standard synthetic aperture radar (SAR) imaging to accurately recover the geometry of the object-under-test. 3-D CS main drawback is the increased calculation time with respect to standard SAR algorithms. To overcome this limitation, a novel technique consisting in splitting a large problem as a combination of small ones is proposed. These small problems can be solved via parallelized 3-D CS, resulting in calculation time savings. Moreover, practical implementation of a 3-D CS-based millimeter-wave imaging system is discussed. Validation with simulations and measurements is presented.

i»?The following contribution aims to test two synthetic aperture radar (SAR)-image-based techniques to obtain geometry and constitutive parameter information about low-permittivity lossless dielectric materials. While characterization of high-permittivity dielectrics is relatively affordable in SAR images, the low amount of power reflected back by lowpermittivity bodies makes their characterization a challenging task. The first proposed technique combines two illumination angles (normal and 45) to overcome the permittivity-geometry uncertainty appearing on each independent case. The second method makes use of the relationship between reflection coefficient amplitudes between two media, which can be directly extracted from the SAR image to provide an estimation of the dielectric object permittivity. Uncertainty of both techniques can be reduced by properly combining them. We validate these methods using a simulation-based application example in the form of a millimeter-wave portal-based system for concealed-object detection.

This paper presents the experimental results for a multistatic millimeter wave synthetic aperture radar (SAR) imaging radar for person security screening applications. By making use of an elliptical toroidal antenna, together with a low-cost, highly integrated, wide-bandwidth 57-64 GHz transceiver chipset, a multistatic imaging system is realized which simplifies the 3D imaging system to a less computationally intense 2D problem. The experimental data is in good agreement with the computed data, imaging not only conventional objects, but also challenging targets such as penetrable dielectrics and dihedrals.

A ray-based reconstruction algorithm for multi-monostatic millimeter-wave radar systems uses time-of-flight data from groups of adjacent transceivers to generate a point cloud representing the target being scanned. Since this reconstruction algorithm uses only the time-of-flight data for specular reflection points, it is less computationally demanding than conventional methods.

We present a novel non-iterative model based on ray analysis to characterize non-metallic, weak dielectric objects (like threat objects) on the surface of a highly conducting background (like the human body) using a focused continuous millimeter-wave sensor. For a simple constant thickness dielectric slab on a ground plane, there are five primary scattering phenomena that must be considered.

A hybrid technique using Digital Breast Tomosynthesis and Microwave Tomography shows promise for improved contrast recovery with respect to conventional breast imaging modalities. A healthy background field is modeled using a Finite Difference solver with prior breast tissue dielectric distribution given by Digital Breast Tomosynthesis. This background field is subtracted from the radar measurements to isolate the anomalous portion of the measured signal which is due to the carcinoma. The healthy background geometry used in this simulation is obtained using a characteristic relationship between the mass attenuation coefficient (measured in X-ray images) of healthy tissue and the complex dielectric constant. This relationship is not known exactly a priori, but can be computed using gradient descent. However, the computationally demanding nature of this inverse problem makes it less desirable for real clinical applications. Proposed herein is an algorithm which uses Born approximation on alternate iterations to decrease computational demand. Preliminary 2D numerical experiments in realistic media show this adjustment decreases the computation time.

A null steering method for Forward-Looking Ground Penetrating Radar (FLGPR) is presented. The radar consists of two arrays (one at L-band and one at X-band) of wideband horns that form synthetic apertures at the vehicle advanced. The null steering method is applied to the SAR vertical view (intensity vs. range and height) image to compensate for the lack of vertical image resolution and help determine if a scatterer is above the ground or buried. First, a target point is selected in the original SAR image, then a null for point spread function (PSF) is calculated for that point. The null PSF is applied to field data to indicate the position of the scatterer. This method is tested with a full-wave scattered field synthetically generated by FDFD, and with field measurement data.

This paper presents a novel interrogation system that combines multiple millimeter wave transmitters and receivers to create real-time high-resolution radar images for personnel security screening. The main novelty of the presented system is that the images can be created as the person being screened continuously moves across a corridor where the transmitters and receivers, working in a fully coherent architecture, are distributed. As the person moves, the transmitters and receivers are sequentially activated to collect data from different angles to inspect the whole body. Multiple images, similar to video frames, are created and examined to look for possible anomalies such as concealed threats. Two-dimensional (2-D) and three-dimensional (3-D) setups have been simulated to show the feasibility of the proposed system. The simulation results in 2-D have been validated using measurements, Ministerio de Ciencia e Innovacin of Spain/FEDER under project MIRIIEM-TEC2014-54005-P, in part by the Gobierno del Principado de Asturias through the PCTI 2013-2017, GRUPIN14-114, in part by the Spanish Government under project TACTICA, in part by the European Regional Development Fund (ERDF), in part by the Galician Regional Government under Projects CN2012/279, CN2012/260 (AtlantTIC) and the Plan I2C (2011–2015), and in part by the Science and Technology Directorate, U.S. Department of Homeland Security under the Award Number 2008-ST-061-ED0001

International audience; Abstract—Improved breast cancer screening technology may allow early detection of the disease. Recently, novel multi-modal techniques have been proposed which take advantage of the high spatial resolution of Digital Breast Tomosynthesis (DBT) and the relative high physical contrast of Microwave Radar (MWR). In this work, the slice-based X-ray images generated by DBT are treated as prior knowledge. The relationship between X-ray attenuation and healthy Microwave dielectric properties (- relationship) is computed using MWR measurements in a modified gradient descent algorithm. Using this relationship, a corresponding distribution of dielectric constants for healthy tissue is generated from the DBT image. The expected electric field for the healthy case is then computed using a computational electromagnetic scattering model. By subtracting this presumed healthy background from the measured field, the component due to any anomalous lesion’s dielectric contrast is isolated. The lesion position is then localized using standard phase-based radar imaging. In this paper, the mathematical justification for this approach is derived. Numerical experiments are presented for which the - relationship is successfully computed and the lesion position is retrieved.

Advanced SAR imaging methods for a dual-band fully polarimetric vehicle-based FLGPR are presented. The radar consists of two arrays (one at L-band and one at X-band) of wideband horns that form synthetic apertures as the vehicle advances. Model-based clutter suppression image processing is used to clean the SAR image obtained from the VV polarized L-band radar by employing a mixed binary mask. This surface clutter mask is formed from the second (X-band) frequency and the VH cross polarized L-band responses, neither of which appreciably scatter from buried objects. In addition, the volumetric wide band point spread function (PSF) which incorporates refraction at the nominal ground surface is determined. These approaches are tested with field-measured data, and subsurface targets are shown to be much easier to detect than by convention surface SAR imaging.

High bit-rate RF communication is shown to be possible in deep oil wells using the air gap between the steel cladding and exterior rock as a dielectric waveguide to propagate TE signals in the kHz to MHz range. Computational modeling analysis in both the frequency and time domains validate the ideal analytic cylindrical guidance condition. Efficient propagation is also possible for asymmetric borehole geometries, even when the casing comes in contact with the surrounding rock wall.

Estudio de configuraciones multiestáticas para mejorar los checkpoints para detección de armas y explosivos basados en sistemas radar., TEC2014-54005-P (MIRIIEM), TEC2014-55290-JIN (PORTEMVISION), CSD2008-00068 (TERASENSE), GRUPIN14-114, U.S. Department of Homeland Security 2008-ST-061-ED0001, National Science Foundation (NSF) EEC-9986821.

Effective communication in oil wells is challenging because of partially-conductive soil and rock, and inhomogeneities in the ground. An alternative approach to communicating with the drill head, or electronics associated with it, is to use the drilled hole itself as a path for communication. The typical well hole is cased with a steel lining and surrounded by air or cement. The electrically-conductive casing, along with a second conductor consisting of the earth itself, acts like a dielectric waveguide used in fiber optics to guide light. The earth is not very conductive, but as long as it has a different dielectric constant than air, it can act to guide waves axially through the gap between the steel and the ground.

Current state-of-the art active millimeter wave radar systems for personnel inspection are based on monostatic or quasi-monostatic configurations that can misrepresent areas of the target when the specular reflection is oriented away from the incident direction. This paper analyzes a fully multistatic mm-wave imaging architecture for human body screening. Transmitters placed off the receiving aperture panel maximize the surface of the human body that can be reconstructed. The effectiveness of this approach is demonstrated with 2D simulations. Three-dimensional simulation-based examples to validate the proposed system using realistic human body models will be presented at the conference.

Synthetic Aperture Radar (SAR) Imaging is able to detect not only conducting but also weak dielectric objects. When a dielectric is affixed to a conducting surface, reflection of the dielectric can be difficult to distinguish from the sidelobes from the surface, since they have similar magnitude and range. The ability to distinguish imaging artifacts and target surfaces is necessary to enable automated feature analysis and threat detection of unknown targets. Our new methods varies the imaging bandwidth, which changes the range of the sidelobe position without changing the range of the dielectric peak reflection, which distinguishes the front surface dielectric reflectivity from sidelobe artifacts. Experimental data is used to demonstrate the effectiveness of this approach.

Ground penetrating radar (GPR) is a viable tool for fast and high fidelity subsurface identification of buried landmines and improvised explosive devices (IEDs). The greatest barrier in GPR surveying systems is the obscuring effect of rough surface clutter. The finite difference frequency domain (FDFD) method can be used to calculate the scattering of the rough terrain. A full 3D FDFD analysis is required but is computationally burdensome when the GPR antennas are distant from the target region. We present an efficient hybrid forward-model to predict the scattering from randomly rough ground surface. In particular, this method models the additive clutter which can then be removed from the observed GPR signals. Our developed forward-looking model compares well with direct FDFD results. The developed model can be used readily for lossy and frequency-dispersive soils.