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This paper explores using Orbital Angular Momentum (OAM) controlled electromagnetic waves for enhanced ground penetrating radar (GPR) imaging and detection. A macroscopic interpretation of OAM is propagating waves with vortex-shaped wave fronts. At the photon level OAM appears as a quantum degree of freedom with integer quanta of angular momentum added to each photon. This is in addition to Spin Angular Momentum (SAM). The use of OAM in GPR has at least two potential advantages. The vortex shape may enable better discernment of cylindrical versus non-cylindrical buried objects. At the quantum level entanglement of OAM with other quantum degrees of freedom may enable enhanced imaging, such as the ghost imaging of objects that produce weak signal returns. The results include experiments that demonstrate the generation and reception of EM waves with a circular pattern of antennas operating as phased arrays to produce vortex-shaped waves at frequencies and dimensions typical of conventional GPRs. 

Ground penetrating radar (GPR) is a remote geophysical sensing method that has been applied in the localization of underground utilities, bridge deck survey, localization of landmines, mapping of terrain for aid in driverless cars, etc. Multistatic GPR can deliver a faster survey, wider spatial coverage, and multiple viewpoints of the subsurface. However, because of the transmit and receive antennas spatial offset, formation of 3D GPR image by simple stacking of the acquired A-scans is inaccurate. Also, averaging of different receivers data may lead to destructive interference of back-scattered waves due to different time delays implied by the spatial offset, so averaging does not lead to higher SNR in general. Furthermore, the energy back-scattered by scatter points are spread in hyperbolas in the GPR raw data. Migration or imaging algorithms are employed to increase SNR by focusing the hyperbolas. This focusing process also leads to better accuracy in target localization. In this paper, a computationally efficient synthetic aperture radar (SAR) imaging algorithm that properly integrates multistatic GPR data in both ground and air-coupled cases is presented. The algorithm is successfully applied on two synthetic datasets. 

Multistatic GPR has the advantages of reducing survey time and leverages more comprehensive data collection. Traditionally in multistatic GPR data processing, the 2D Bscan image obtained from each receive antenna are simply stacked for 3D image reconstructions. However, such approach is typically inadequate as the multistatic GPR receivers are mounted with spatial offsets, causing back-scattering signals from the same target to have differing time of arrivals. For proper fusion of multistatic GPR data, migration methods that consider the transmitters and receivers spatial offset and data variations among different receiving antennas may be employed. In this study, the back-projection algorithm (BPA) is investigated. The algorithm consists of determining the wave travel path and associated travel time, and projecting the corresponding signal value back into space domain. Furthermore, antenna radiation pattern is incorporated. The BPA enables scatter shape reconstruction and is prone to parallel computing. For validation, multistatic GPR 3D tomographic image reconstruction is successfully applied to laboratory data. 

Digital three-dimensional (3-D) information concerning the location and condition of subsurface urban infrastructure is emerging as a potential new paradigm for aiding in the assessment, construction, emergency response, management, and planning of these vital assets. Subsurface infrastructure encompasses utilities (water, stormwater, wastewater, gas, electricity, telecommunications, steam, etc.), geotechnical formations, and the built underground (including tunnels, subways, garages and subsurface buildings). Traditional approaches for collecting location information include merging as-built drawings, historical records, and dead reckoning; and combining with information gathered by above-ground geophysical instruments, such as ground penetrating radars, magnetometers and acoustic sensors. This paper presents results of efforts aimed at using photogrammetric and augmented reality (AR) techniques to aid collecting, processing, and presenting 3-D location information. 

Ground penetrating radar (GPR) subsurface sensing is a promising nondestructive evaluation (NDE) technique for inspecting and surveying underground utilities in complex urban environments, as well as for monitoring other key infrastructure such as bridges and railroads. A challenge of such technique lies on image formation from the recorded GPR data. In this work, a fast back projection algorithm (BPA) for three-dimensional GPR image construction is explored. The BPA is a time-domain migration method that has been effectively used in GPR image formation. However, most of the studies in the literature apply a computationally intensive BPA to a two-dimensional dataset under the assumption that an in-plane scattering occurs underneath the GPR antennas. This assumption is not precise for 3D GPR image formation as the GPR radiation scatters in multiple directions as it reaches the ground. In this study, a generalized form for an approximation to determine the scattering point in an air-coupled GPR system is developed which considerably reduces the required computations and can accurately localize the scattering point position. The algorithm is evaluated by applications on GPR data synthesized using GprMax, a finite-difference time domain (FDTD) simulator. 

The development of modern cities heavily relies on the availability and quality of underground utilities that provide drinking water, sewage, electric power, and telecommunication services to sus- tain its growing population. However, the information of localiza- tion and condition of subterranean infrastructures is generally not readily available, especially in areas with congested pipes, which impacts urban development, as poorly documented pipes may be hit during construction, affecting services and causing costly de- lays. Furthermore, aging components are prone to failure and may lead to resources waste or the interruption of services. Ground penetrating radar (GPR) is a promising remote sensing technique that has been recently used for mapping and assessment of under- ground infrastructure. However, current commercial GPR survey systems are designed with wheel-encoders or GPS for positioning. Wheel-encoder based GPR surveys are restrained to linear-route only, preventing the use of GPR for accurate localization of city wide underground infrastructure inspection. While GPS signal is degraded in urban canyons and unavailable in city tunnels. In this work, we present a new GPR system integration with augmented reality (AR) based positioning that can overcome the limitations of current GPR systems to enable arbitrary-route scanning with a high fidelity. It has the potential for automation of GPR survey and integration with AR smartphone applications that could be used for better planning in urban development.