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Control of orbital angular momentum (OAM) offers the potential for increases in control, sensitivity, and security for high-performance microwave systems. OAM is characterized by an integer OAM mode where zero represents the case of a plane wave. Microwaves with a nonzero OAM mode propagate with a helical wavefront. Orthogonal OAM modes can be used to carry distinct information at the same frequency and polarization, increasing the data rate. The OAM waveform may also increase radar detection capability for certain shaped objects. OAM can be induced by broadcasting a plane wave through a spatial phase plate (SPP) dielectric which introduces an azimuthally dependent phase delay. However, SPPs are frequency-specific, which presents an obstacle for harnessing OAM in frequency-modulated communication systems and wide-bandwidth radar. In this study, we develop a circular phased array to synthesize the desired vortex-shaped wavefront. This approach offers a critical advantage: the phases of all antenna elements are easily programmable under different frequencies. As a result, transmission and reception of the OAM beam can be controlled with great flexibility, making it operable over a wide frequency spectrum, which leverages OAM radar functionality and performance. In this paper, we will investigate a wide-bandwidth radar with OAM mode-control and signal processing. 

Successful maintenance and development of underground infrastructures depends on the ability to access underground utilities efficiently. In general, obtaining accurate positions and conditions of subterranean utilities is not trivial due to inaccurate data records and occlusions that are common in densely populated urban areas. Limited access to underground resources poses challenges to underground utilities management. Ground penetrating radar (GPR) is an effective sensing tools widely used for underground sensing. Combining high accuracy GPR data and augmented reality (AR) poses enables accurate real time visualizations of the buried objects. Although GPR and AR collect and visualize high accuracy data, intensive computation is required. This work presents a novel GPR-AR system that decreases post-processing time significantly while maintaining a neutral format across GPR-AR data collection methods regardless of varying Internet or GPS connection strengths. The methods explored in this work to mitigate failures of previous systems include automated and georeferenced post processing, the classification of underground assets using artificial intelligence, and real time data collection path visualizations. This work also lays a foundation for the potential combinations of a 5G GPR-AR system in which the temporal gap between data collection and visualization can be alleviated. 

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.