The theme of this dissertation is to advance safety hazard mitigation by detecting and characterizing hidden targets of concern. Ground-penetrating radar (GPR) is used to detect and characterize hidden targets that pose safety hazards at Earth's surface, within shallow soil, and within rock. The resulting images detect unexploded ordnance (UXO) and detect fractures that pose collapse hazards in a mine. Detecting and characterizing fractures and voids within rock prior to excavation can enable mitigation of mine collapse hazards. GPR data were acquired on the wall of a pillar in an underground mine. Strong radar reflections in the field data correlate with fractures and a cave exposed on the pillar walls. Pillar wall roughness was included in migration, a wavefield imaging algorithm, to quantitatively locate fractures and voids and map their spatial relationships within the rock. Quantifying the radar reflection amplitudes enabled mapping the distance between fracture walls. Detecting and characterizing UXO and landmines from a safe distance can enable de-mining. Migration was used to improve GPR imaging for unmanned aerial vehicle (UAV) data acquisitions. Existing algorithms were adapted for UAV flight irregularities and surface topography, and a new algorithm was developed that does not depend on the unknown soil wavespeed. Errors associated with wavespeed and raypath assumptions were quantified. The algorithms were tested with real and synthetic datasets. The improved and new algorithms are more successful than previous algorithms. To detect linear targets at all orientations, fully polarized GPR data are needed. Polarity combinations were investigated to optimize the detection of surface and subsurface small targets and linear targets. Scattering caused by topographic roughness is the primary shallow subsurface noise. For subsurface targets, detection is optimized by migration plus a polarity combination that captures all scattered energy. Strong reflection and scattering from the air-ground boundary can hide surface targets. Detection is optimized by removing the strong isotropic surface scattering, imaging targets by their anisotropic scattering. / Doctor of Philosophy / The theme of this dissertation is to advance safety hazard mitigation by detecting and characterizing hidden targets of concern. Ground-penetrating radar (GPR) is used to detect and characterize hidden targets that pose safety hazards at Earth's surface, within shallow soil, and within rock. The resulting images detect unexploded ordnance (UXO)/landmines and detect fractures that pose collapse hazards in a mine. Detecting and characterizing fractures and voids within rock prior to mining can enable mitigation of mine collapse hazards. GPR data were acquired on the wall of a pillar in an underground mine. Strong radar reflections in the field data correlate with fractures and a cave exposed on the pillar walls. Pillar wall roughness was included in migration, a wavefield imaging algorithm, to quantitatively locate fractures and voids and map their spatial relationships within the rock. Quantifying the radar reflection amplitudes enabled mapping the distance between fracture walls. Detecting and characterizing UXO, landmines from a safe distance can enable de-mining. Migration was used to improve GPR imaging for an unmanned aerial vehicle (drone) data acquisition. Existing algorithms were adapted for drone flight irregularities and surface topography, and a new algorithm was developed that does not depend on the unknown soil properties. Errors associated with the algorithms' assumptions were quantified. The algorithms were tested with real and computer-generated datasets. The improved and new algorithms are more successful than previous algorithms. To detect all targets regardless of their orientation, GPR data need to be acquired with antenna pointing in multiple directions (different polarities). Polarity combinations were investigated to optimize the detection of surface and subsurface small targets and linear targets. Scattering caused by topographic roughness is the primary shallow subsurface noise. For subsurface targets, detection is optimized by migration plus a polarity combination that captures all scattered energy. Strong radar reflection from the air-ground boundary can hide surface targets. Detection is optimized by removing the strong ground surface from the data, and imaging targets by differences in their radar scattering.

The use of magnetometers combined with unmanned aerial vehicles (UAVs) is an emerging market for commercial and military applications. This study presents the methodology used to magnetically characterize a novel fixed-wing vertical take-off and landing (VTOL) UAV. The most challenging aspect of integrating magnetometers on manned or unmanned aircraft is minimizing the amount of magnetic noise generated by the aircraft’s onboard components. As magnetometer technology has improved in recent years magnetometer payloads have decreased in size. As a result, there has been an increase in opportunities to employ small to medium UAV with magnetometer applications. However, in comparison to manned aviation, small UAVs have smaller distance scales between sources of interference and sensors. Therefore, more robust magnetic characterization techniques are required specifically for UAVs. This characterization determined the most suitable position for the magnetometer payload by evaluating the aircraft’s static-field magnetic signature. For each aircraft component, the permanent and induced magnetic dipole moment characteristics were determined experimentally. These dipole characteristics were used to build three dimensional magnetic models of the aircraft. By assembling the dipoles in 3D space, analytical and numerical static-field solutions were obtained using MATLAB computational and COMSOL finite element analysis frameworks. Finally, Tolles and Lawson aeromagnetic compensation coefficients were computed and compared to evaluate the maneuver noise for various payload locations. The magnetic models were used to study the sensitivity of the aircraft configuration and to simultaneously predict the effects at potential sensor locations. The study concluded by predicting that a wingtip location was the area of lowest magnetic interference. / Graduate