Assessment of down-looking GPR sensors for landmine detection

Rosen, Eric; Ayers, Elizabeth

doi:10.1117/12.603831

Cited by 13 publications

(7 citation statements)

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“…An existing SCR metric developed at the Institute for Defense Analyses specifically for GPR data, KSUM [7], is effective at providing this information. KSUM is a dimensionless metric that provides a value for each GPR A-scan.…”

Section: Introductionmentioning

confidence: 99%

GPR preprocessing optimization with signal-to-clutter metrics

et al. 2013

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Prior to the calculation of target detection features, ground penetrating radar (GPR) data typically requires extensive preprocessing to suppress clutter artifacts and enhance signals corresponding to weaker targets. Optimization of this GPR signal preprocessing pipeline is necessary to provide the best opportunity at visual detection and automatic target recognition. Manual, independent adjustment of the many configuration parameters in the data preprocessing pipeline is inefficient and not guaranteed to find an optimal result. In this paper, the authors present a new metric for GPR processed data quality and demonstrate its utility in an automated parameter sweep optimization of a large set of algorithm configuration parameters. The observed costs and benefits of using automated preprocessing optimization are presented and discussed.For preprocessing optimization and evaluation, a cost function was desired that is independent of the target detection features -to enable independent evaluation of the various components of the GPR target detection software. The proposed cost function, JSUM, is a signal-to-clutter ratio (SCR) metric, derived from the known KSUM metric. JSUM was developed to be sensitive to a particular type of noise in GPR data not captured by KSUM. The response of JSUM and KSUM to different common types of noise was explored to qualify the usefulness of the metric.JSUM was used as the cost function for a parameter sweep optimization across a set of preprocessing parameters. The outcomes of this optimization are presented for discussion.

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Section: Introductionmentioning

confidence: 99%

GPR preprocessing optimization with signal-to-clutter metrics

et al. 2013

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“…The latter avoid the interaction with the soil, but the strong obscuring clutter (due to impedance mismatch at the rough air-soil interface) greatly compromises the detection of buried targets. GPR systems can also be classified as Forward-Looking GPR (FLGPR) [4] and Down-Looking GPR (DLGPR) [5]. In vehicle mounted FLGPR systems, the antennas look ahead of a vehicle, with an angle of incidence that helps to maximize TM (Transverse Magnetic) waves penetration into the soil and/or to minimize reflections from the air-soil interface backscattered to the receiver.…”

Section: Introductionmentioning

confidence: 99%

Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors

et al. 2019

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This work proposes a novel Ground Penetrating Radar (GPR) system to detect landmines and Improvised Explosive Devices (IEDs). The system, which was numerically evaluated, is composed of a transmitter placed on a vehicle and looking forward and a receiver mounted on a drone and looking downwards. This combination offers both a good penetration and a high resolution, enabling the detection of non-metallic targets and mitigating the clutter at the air–soil interface. First, a fast ray tracing simulator was developed to find proper configurations of the system. Then, these configurations were validated using a full wave simulator, considering a flat and a rough surface. All simulations were post-processed using a fast and accurate Synthetic Aperture Radar (SAR) algorithm that takes into account the constitutive parameters of the soil. The SAR images for all configurations were compared, concluding that the proposed contribution greatly improves the target detection and the surface clutter reduction over conventional forward-looking GPR systems.

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“…In the former, the antennas look ahead of the vehicle, reducing reflections from the ground surface at the expense of a lower resolution [3]. In the latter, the antennas are perpendicular to the soil, achieving high resolution but suffering from strong specular reflections [4].…”

Section: Introductionmentioning

confidence: 99%

UAV-mounted GPR for NDT applications

García-Fernández

Álvarez-López

González-Valdés

et al. 2018

2018 15th European Radar Conference (EuRAD)

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This contribution introduces a novel airborne system for subsurface sensing and imaging applications. The system consists of a Ground Penetrating Radar (GPR) mounted on an Unmanned Aerial Vehicle (UAV). Since the system does not need to be in contact with the soil, it is particularly useful for some Non-Destructive Testing (NDT) applications such as landmine detection or archeological surveys. An overview of the system and some of the first flight tests are shown in this contribution. The results of these flight tests prove the feasibility of the system to detect both metallic and dielectric targets. Furthermore, since the system includes a high-accuracy positioning system, measurements could be coherently combined to improve the radar image resolution.

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Assessment of down-looking GPR sensors for landmine detection

Cited by 13 publications

References 0 publications

GPR preprocessing optimization with signal-to-clutter metrics

GPR preprocessing optimization with signal-to-clutter metrics

Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors

UAV-mounted GPR for NDT applications

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