A Through-the-Wall Radar Imaging Method Based on a Realistic Model

An image focusing method based on a realistic model for a wall is proposed for through-the-wall radar imaging using a multiple-input multiple-output array. A technique to estimate the wall parameters (i.e., position, thickness, and permittivity) from the radar returns is developed and tested. The estimated wall properties are used in the developed penetrating image formation to form images. The penetrating image formation developed is computationally efficient to realize real-time imaging, which does not depend on refraction points. The through-the-wall imaging method is validated on simulated and real data. It is shown that the proposed method provides high localization accuracy of targets concealed behind walls

Computational polarimetric microwave imaging

We propose a polarimetric microwave imaging technique that exploits recent advances in computational imaging. We utilize a frequency-diverse cavity-backed metasurface, allowing us to demonstrate high-resolution polarimetric imaging using a single transceiver and frequency sweep over the operational microwave bandwidth. The frequency-diverse metasurface imager greatly simplifies the system architecture compared with active arrays and other conventional microwave imaging approaches. We further develop the theoretical framework for computational polarimetric imaging and validate the approach experimentally using a multi-modal leaky cavity. The scalar approximation for the interaction between the radiated waves and the target---often applied in microwave computational imaging schemes---is thus extended to retrieve the susceptibility tensors, and hence providing additional information about the targets. Computational polarimetry has relevance for existing systems in the field that extract polarimetric imagery, and particular for ground observation. A growing number of short-range microwave imaging applications can also notably benefit from computational polarimetry, particularly for imaging objects that are difficult to reconstruct when assuming scalar estimations.Comment: 17 pages, 15 figure

Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction and multipath clutter in indoor through-wall environments. While several methods have been proposed for removing target independent static and dynamic clutter, there still remain considerable challenges in mitigating target dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this work we focus on mitigating wall effects using a machine learning based solution -- denoising autoencoders -- that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with two-dimensional array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with two-dimensional array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images

3-D Microwave Imaging for Breast Cancer

We introduce a novel microwave imaging technique for breast cancer detection. Our approach provides a one-pass inverse image solution, which is completely new and unprecedented, unrelated to tomography or radar-based algorithms, and unburdened by the optimization toil which lies at the heart of numerical schemes. It operates effectively at a single frequency, without requiring the bandwidth of radar techniques. Underlying this new method is our unique Field Mapping Algorithm (FMA), which transforms electromagnetic fields acquired upon one surface, be it through outright measurement or some auxiliary computation, into those upon another in an exact sense

Eclipsing Light-Curve Asymmetry for Black-Hole Accretion Flows

We propose an eclipsing light-curve diagnosis for black-hole accretion flows. When emission from an inner accretion disk around a black hole is occulted by a companion star, the observed light curve becomes asymmetric at ingress and egress on a time scale of 0.1-1 seconds. This light-curve analysis provides a means of verifying the relativistic properties of the accretion flow, based on the special/general relativistic effects of black holes. The ``skewness'' for the eclipsing light curve of a thin disk is $\sim 0.08$, whereas that of a slim disk is $\sim 0$, since the innermost part is self-occulted by the disk's outer rim.Comment: 7 pages, 4 figures, PASJ accepte

Increased motor cortex excitability for concealed visual information

Deceptive behaviour involves complex neural processes involving the primary motor cortex. The dynamics of this motor cortex excitability prior to lying are still not well understood. We sought to examine whether corticospinal excitability can be used to suggest the presence of deliberately concealed information in a modified version of the Guilty Knowledge Test (GKT). Participants pressed keys to either truthfully or deceitfully indicate their familiarity with a series of faces. Motor-evoked-potentials (MEPs) were recorded during response preparation to measure muscle-specific neural excitability. We hypothesised that MEPs would increase during the deceptive condition not only in the lie-telling finger but also in the suppressed truth-telling finger. We report a group-level increase in overall corticospinal excitability 300 ms following stimulus onset during the deceptive condition, without specific activation of the neural representation of the truth-telling finger. We discuss cognitive processes, particularly response conflict and/or automated responses to familiar stimuli, which may drive the observed non-specific increase of motor excitability in deception

Synthetic aperture imaging with intensity-only data

We consider imaging the reflectivity of scatterers from intensity-only data recorded by a single moving transducer that both emits and receives signals, forming a synthetic aperture. By exploiting frequency illumination diversity, we obtain multiple intensity measurements at each location, from which we determine field cross-correlations using an appropriate phase controlled illumination strategy and the inner product polarization identity. The field cross-correlations obtained this way do not, however, provide all the missing phase information because they are determined up to a phase that depends on the receiver's location. The main result of this paper is an algorithm with which we recover the field cross-correlations up to a single phase that is common to all the data measured over the synthetic aperture, so all the data are synchronized. Thus, we can image coherently with data over all frequencies and measurement locations as if full phase information was recorded

Development of Scale and Rotation Invariant Neural Network based Technique for Detection of Dielectric Contrast Concealed Targets with Millimeter Wave System

The detection of concealed targets beneath a person’s clothing from standoff distance is an important task for protection and the security of a person in a crowded place like shopping malls, airports and playground stadium, etc. The detection capability of the concealed weapon depends on a lot of factors likes, a collection of back scattered data, dielectric property and a thickness of covering cloths, the hidden object, standoff distance and the probability of false alarm owing to objectionable substances. Though active millimeter wave systems have used to detect weapons under cloths, but still more attention is required to detect the target likes a gun, knife, and matchbox. To observe such problems, active V-band (59 GHz- 61 GHz) MMW radar with the help of artificial neural network (ANN) has been demonstrated for non-metallic as well as metallic concealed target detection. To validate ANN, the signature of predefined targets is matched with the signature of validated data with the help of the correlation coefficient. The proposed technique has good capability to distinguish concealed targets under various cloths.