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This paper presents research concerning the use of visual-inertial Simultaneous Localization And Mapping (SLAM) algorithms to aid in Continuous Wave (CW) radar target mapping. SLAM is an established field in which radar has been used to internally contribute to the localization algorithms. Instead, the application in this case is to use SLAM outputs to localize radar data and construct three-dimensional target maps which can be viewed live in augmented reality. These methods are transferable to other types of radar units and sensors, but this paper presents the research showing how the methods can be applied to calculate depth efficiently with CW radar through triangulation using a Boolean intersection algorithm. Localization of the radar target is achieved through quaternion algebra. Due to the compact nature of the SLAM and CW devices, the radar unit can be operated entirely handheld. Targets are scanned in a free-form manner where there is no need to have a gridded scanning layout. The main advantage to this method is eliminating many hours of usage training and expertise, thereby eliminating ambiguity in the location, size and depth of buried or hidden targets. Additionally, this method grants the user the additional power, penetration and sensitivity of CW radar without the lack of range finding. Applications include pipe and buried structure location, avalanche rescue, structural health monitoring and historical site research. 

Ultra-wideband (UWB) ground penetrating radar (GPR) is an effective, widely used tool for detection and mapping of buried targets. However, traditional ground penetrating radar systems struggle to resolve and identify congested target configurations and irregularly shaped targets. This is a significant limitation for many municipalities who seek to use GPR to locate and image underground utility pipes. This research investigates the implementation of orbital angular momentum (OAM) control in an UWB GPR, with the goal of addressing these limitations. Control of OAM is a novel technique which leverages an additional degree of freedom offered by spatially structured helical waveforms. This paper examines several free-space and buried target configurations to determine the ability of helical OAM waveforms to improve detectability and distinguishability of buried objects including those with symmetric, asymmetric, and chiral geometries. Microwave OAM can be generated using a uniform circular array (UCA) of antennas with phase delays applied according to azimuth angle. Here, a four-channel network analyzer transceiver is connected to a UCA to enable UWB capability. The characteristic phase delays of OAM waveforms are implemented synthetically via signal processing. The viability demonstrated with the method opens design and analysis degrees of freedom for penetrating radar that may help with discerning challenging targets, such as buried landmines and wires. 

This paper presents research on the use of penetrating radar combined with 3-D computer vision for real-time augmented reality enabled target sensing. Small scale radar systems face the issue that positioning systems are inaccurate, non-portable or challenged by poor GPS signals. The addition of modern computer vision to current cutting-edge penetrating radar technology expands the common 2-D imaging plane to 6 degrees of freedom. Applying the fact that the radar scan itself is a vector with length equivalent to depth from the transmitting and receiving antennae, these technologies used in conjunction can generate an accurate 3-D model of the internal structure of any material for which radar can penetrate. The same computer vision device that localizes the radar data can also be used as the basis for an augmented reality system. Augmented reality radar technology has applications in threat detection (human through-wall, IED, landmine) as well as civil (wall and door structure, buried item detection). For this project, the goal is to create a data registration pipeline and display the radar scan data visually in a 3-D environment using localization from a computer vision tracking device. Processed radar traces are overlayed in real time to an augmented reality screen where the user can view the radar signal intensity to identify and classify targets. 

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. 

Location and identification of subterranean infrastructure is crucial for managing and maintaining urban infrastructure and utility, and locating subsurface hazards. Low-frequency oscillating magnetic fields suffer less attenuation due to propagating media than ground penetrating radar. Here, electronically-geared, rotating neodymium magnets project oscillating magnetic fields which are manipulated to provide object identification from rapid analysis of dynamic magnetometer data. Ferromagnetic materials interact directly with the rotating magnetic field. Eddy currents, which induce a counter-propagating magnetic field, are generated in conductive, non-ferromagnetic materials. Two applications are highlighted by preliminary experiments: discrimination between copper, aluminum and steel pipes, and improved detection of buried explosive devices.