Handheld Ground Penetrating Radar Research Articles

Unidirective Miniaturized Ultrawideband Antenna for Sensing Buried Objects in Handheld Ground-Penetrating Radar Systems: A design approach

Ground-penetrating radars (GPR) using ultrawideband (UWB) technology have attracted a lot of research interests worldwide for high-resolution sensing and material characterization. This research contribution, therefore, introduces a miniaturized lightweight UWB antenna with high-gain characteristics for applications in handheld GPR systems. The high-gain antenna is developed to function in the 3.13–11.74-GHz wide frequency range such that it can allow high-resolution sensing of buried targets, such as landmines, metal pipes, and other structures. An elliptical quadrant-shaped UWB monopole radiator with a protuberant ground plane is conceptualized to constitute a highly miniaturized antenna of size 18 × 11 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The standalone UWB antenna realizes an impedance bandwidth of 3.07–11.67 GHz with a peak gain of 2.9 dBi. Next, a single-side copper-coated dielectric substrate as a reflector is placed beneath the antenna to attain a significant gain improvement. The proposed design with a reflector provides an average gain improvement of about 7.1 dB without influencing the original bandwidth. For experimental validation, a prototype is fabricated and characterized. The measured performance reveals close agreement with the simulation with an operating bandwidth of 3.19–11.83 GHz and a maximum gain of approximately 10 dBi at 4.02 GHz.

Estimation of Handheld Ground-Penetrating Radar Antenna Position with Pendulum-Model-Based Extended Kalman Filter

Landmines and explosive remnants of war are a significant threat in tens of countries and other territories, causing the deaths or injuries of thousands of people every year, even long after military conflicts. Effective technical means of remote detecting, localizing, imaging, and identifying mines and other buried explosives are still sought and have a great potential utility. This paper considers a positioning system used as a supporting tool for a handheld ground penetrating radar. Accurate knowledge of the radar antenna position during terrain scanning is necessary to properly localize and visualize the shape of buried objects, which helps in their remote classification and makes demining safer. The positioning system proposed in this paper uses ultrawideband radios to measure the distances between stationary beacons and mobile units. The measurements are processed with an extended Kalman filter based on an innovative dynamics model, derived from the model of a pendulum motion. The results of simulations included in the paper prove that using the proposed pendulum dynamics model ensures a better accuracy than the accuracy obtainable with other typically used dynamics models. It is also demonstrated that our positioning system can estimate the radar antenna position with the accuracy of single centimeters which is required for appropriate imaging of buried objects with the ground penetrating radars.

3D Migration Depth Focus Velocity Analysis of Hand-Held Ground Penetrating Radar

Hand-held ground penetrating radar (GPR) systems have been widely applied to landmine detections during recent decades. The accuracy of an imaging result by migration for a hand-held GPR is strongly related to the accuracy of subsurface velocity distribution obtained from multi offset data. For shallow targets like landmines, the hyperbolas are usually not distinct in 2D slices and are masked by the surface reflections. In this article, we propose a 3D migration depth focus velocity analysis method for hand-held GPRs to estimate the background velocity of the subsurface. This method is performed based on the images generated by migrations. The objective function is defined as the proportion of the target on the depth slice containing the target. After migrating a GPR radargram with different velocities, the background velocity, which minimizes the objective function, can be determined by comparing the imaging results by migration using different velocities. To test the proposed method, we apply this procedure to experimental GPR data collected with an advanced landmine imaging system (ALIS) in the laboratory. Subsequently, the velocity of the background is obtained, 3D diffraction migration with the obtained velocity achieves subsurface imaging with high quality. The accurate position and depth of the target are obtained from the optimal migration image.

The Use of GPR and Microwave Tomography for the Assessment of the Internal Structure of Hollow Trees

Internal decays in trees can rapidly escalate into a full decomposition of the inner structural layer, i.e., the “heartwood” layer, due to the action of aggressive diseases and fungal infections. This process leads to the formation of big cavities and hollows, which remain surrounded by the sapwood layer only. Estimating the thickness of the sapwood layer with a high degree of accuracy is therefore crucial for correct assessment of the structural integrity of hollow trees, as well as an extremely challenging task. In this context, ground-penetrating radar (GPR) has proven effective in providing details of the internal structure of trees. Nevertheless, the existing GPR processing methods still offer limited information on their internal configuration. This study investigates the effectiveness of GPR enhanced by a microwave tomography inversion approach in the assessment of hollow trees. To this aim, a living hollow tree was investigated by performing a set of pseudocircular scans along the bark perimeter with a hand-held common-offset GPR system. The tree was then felled, and sections were cut for testing purposes. A dedicated data processing framework was developed and tested through numerical simulations of hollow tree sections. The internal structure of the real trunk was therefore reconstructed via a tomographic imaging approach and the outcomes were quantitatively analyzed by way of comparison with the real sections’ main geometric features. The tomographic approach has proven very accurate in locating the sapwood–cavity interface and in the evaluation of the sapwood layer thickness, with a centimeter prediction accuracy.

The roles of macro‐ and micro‐scale geophysical investigations to guide and monitor excavations at a Middle Woodland site in northern Georgia, USA

AbstractGeophysical investigations have become standard in archaeological practice to map sites and help select location for excavation, but the application of these techniques in real time during excavation to help anticipate feature location and maximize recovery has not been developed. This paper presents results from both traditional geophysics and new approaches to using these methods during excavations at Rice Farm (9DW276), a Middle Woodland site located in a broad floodplain adjacent to the Etowah River in north Georgia. Ground penetrating radar (GPR) at the surface was effective in recording reflection events indicative of archaeological features, such as hearths, posts and possibly ditches. Magnetometry was helpful, but less effective due to heavy plough zone scarring and noise from modern metallic debris. High‐frequency handheld GPR was helpful in monitoring excavations in real time and assisted excavators in anticipating the locations of both large and small diameter features. Excavation of geophysical anomalies exposed important features for the interpretation of this newly documented site.

Ground Surface Reflection Compensation for Hand-Held GPR

In landmine detection by a hand-held ground penetrating radar (GPR) sensor, GPR signal is suffered from the unstable stand-off distance between the antenna and the rough ground surface. We propose in this letter a method to compensate the irregularity in the arrival time of the ground reflection in GPR and applied it to data sets acquired in minefields in Cambodia. Selecting the frequency with the highest power of the ground surface reflection, the phase of the received signal at the frequency is equalized to compensate the irregular reflection. We demonstrate that the ground reflection signal became uniform and confirmed the quality of the shape of the target can be improved.

Detection and localization of rebar in concrete by deep learning using ground penetrating radar

Ground penetrating radar (GPR) has been widely used for detection and localization of reinforced steel bar (rebar) in concrete in a non-destructive way. However, manual interpretation of a large number of GPR images is time-consuming, and the results highly depend on practitioner experience and the available priori information. This paper proposes an automatic detection and localization method using deep learning and migration. Firstly, a Single Shot Multibox Detector (SSD) model is established to identify regions of interest containing hyperbolas in a GPR image. This deep learning model is trained using a real GPR dataset, which contains 13,026 rebar targets in 3992 images, collected on residential buildings under construction. Secondly, each target region is migrated and transformed into a binary image to locate the rebar. After the binarization, the apex of the focused cluster is obtained and used to estimate both the horizontal position and the depth of the rebar. The testing results show that the detection accuracy of the proposed artificial intelligence method is 90.9%. The computation time needed for processing a GPR image with a size of 300 × 300 pixels is only 0.47 s. The depth estimation error in a laboratory experiment is <1.5 mm (5%), and the lateral position error is <0.7 cm. Therefore, it is concluded that the proposed method can automatically detect the rebar from GPR images in real time when a handheld GPR system is operated at a walking speed and the depth estimation accuracy is acceptable in practice.

Buried Target Parameter Characterization for Handheld Ground Penetrating Radar

Buried Target Parameter Characterization for Handheld Ground Penetrating Radar

Improvement of Handheld Radar Operators’ Hazard Detection Performance Using 3-D Visualization

Handheld ground-penetrating radar systems are employed in both military and humanitarian demining operations. Radar system operators are given the difficult job of determining the nature of subsurface objects from signal reflections in real time. Current systems require operators to multitask both collection and classification. This letter tested a 3-D data visualization method against a 2-D method. The 3-D method attempts to separate tasks by not forcing operators to classify objects in real time. Data showed that users classifying objects with the 3-D system had better performance and also reported that this system was more user-friendly. In addition, users were able to classify underground objects quicker with the 3-D system. The results of this letter demonstrate the benefit of 3-D visualization in ground scanning systems in increasing performance and decreasing cognitive load.

FPGA and ARM based Ultra-Low Power Solution for Signal Processor and Image Processor for Ground Penetrating Radar

In conventional radar applications, signal processor and image processor are realized as separate individual cards. Handheld Ground Penetrating radar is a compact, ultra-low power battery operated system. This system is operating on a small battery for eight hours of continuous operation. So utmost care is required in developing every sub-system in terms of power consumption. This paper describes the design steps in realizing an ultra-low power signal processor and image processor card for handheld Ground Penetrating Radar on a single PCB.

A consistent approach for processing and interpretation of data from concrete bridge members collected with a hand-held GPR device

Non-destructive testing (NDT) techniques have become important means to inspect concrete bridge members. In particular, ground penetrating radar (GPR) has the ability to provide valuable information with respect to geometry and location of reinforcing bars, and, in some cases, areas of deterioration such as corrosion or potential delaminations. In this paper, we present a consistent methodology to process and interpret data from a hand-held GPR device. These small devices are often used by practitioners to more accurately inspect areas of interest while traffic control is maintained. Our methodology uses an exact non-linear time-depth relationship and cross-correlation to locate and visualize reflectors. The polarity and amplitude of the reflected pulse is also extracted and gives further information on the type of reflector, e.g., air vs. steel. Data collected and processed from three laboratory mock-up specimens are presented and discussed, and further work is proposed.

Subsurface Imaging Using a Handheld GPR MD System

A multisensor system could offer an effective solution to land-mine detection. A handheld dual-sensor system with a position tracking system is developed, which can acquire ground-penetrating radar (GPR) and metal detector (MD) data with 2-D space coordinates. However, as the measurement points are random and data are irregular for the human operator, it is difficult to display subsurface visualization imaging. We develop a set of techniques to achieve visualization imaging of both GPR and MD. A modified migration algorithm with continuous-root-mean-square velocity is proposed to process irregular GPR data, and an interpolation algorithm is recommended to process irregular MD data. In an application to field experiment data, clear subsurface targets imaging was achieved in both GPR slice and MD image.

Hand-Held GPR Imaging Using Migration for Irregular Data

Handheld ground-penetrating radar (GPR) system is one of a number of technologies that has been researched as a means of improving landmine detection efficiency. But it is difficult to display landmine imaging in horizontal slices, because human being operator cannot collect regular GPR data along the survey line and the measurement positions are random. Also detection of buried landmines by GPR normally suffers from very strong clutter that will decrease the image quality. So we proposed two processing procedures to achieve hand-held GPR subsurface imaging. One processing procedure is composed of interpolation and migration algorithm. The interpolation is chosen to produce the regular grid data set. The migration is used to improve signal-clutter ratio and reconstruct subsurface imaging. In the other processing procedure we developed a simply modified migration algorithm that can directly processes irregular GPR data. Two processing procedures, migration with interpolation and modified migration without interpolation, were applied to a field experiment data respectively. Both of processing procedures can efficiently process irregular GPR data and achieve subsurface imaging of high quality. The imaging processed by migration with interpolation is used in the dual sensor, advanced landmine imaging system (ALIS), and two sets of ALIS have detected more than 80 AP-mines since 2009. Also the modified migration algorithm can save the processing time, which is important for field real-time signal processing. We concluded that to achieve clear hand-held GPR imaging, the interpolation is not necessary algorithm and the migration is the key algorithm.

A Least Squares Approach to Buried Object Detection Using Ground Penetrating Radar

In this study, we utilize the least squares formulation to solve the real-time buried object detection problem. Least squares estimation is used to estimate the next ground penetrating radar (GPR) signal from previous samples, where there is no underground object. If the measured GPR signal is considerably different than the estimated signal, presence of an underground object is concluded. In order to attain real-time performance, Cholesky factorization is used when solving the linear systems. The proposed approach is tested on an extensive data set of different surrogate mines and other objects that are commonly encountered under the ground. The data are collected from three different terrains with different soil types to reveal the true performance of the method. It is demonstrated that our approach achieves almost 100% performance with a false alarm rate of approximately 10% on real GPR data collected with a handheld GPR system.

Surface and borehole ground-penetrating-radar developments

During the past [Formula: see text], ground-penetrating radar (GPR) has evolved from a skeptically received glacier sounder to a full multicomponent 3D volume-imaging and characterization device. The tool can be calibrated to allow for quantitative estimates of physical properties such as water content. Because of its high resolution, GPR is a valuable tool for quantifying subsurface heterogeneity, and its ability to see nonmetallic and metallic objects makes it a useful mapping tool to detect, localize, and characterize buried objects. No tool solves all problems; so to determine whether GPR is appropriate for a given problem, studying the reasons for failure can provide an understanding of the basics, which in turn can help determine whether GPR is appropriate for a given problem. We discuss the specific aspects of borehole radar and describe recent developments to become more sensitiveto orientation and to exploit the supplementary information in different components in polarimetric uses of radar data. Multicomponent GPR data contain more diverse geometric information than single-channel data, and this is exploited in developed dedicated imaging algorithms. The evolution of these imaging schemes is discussed for ground-coupled and air-coupled antennas. For air-coupled antennas, the measured radiated wavefield can be used as the basis for the wavefield extrapolator in linear-inversion schemes with an imaging condition, which eliminates the source-time function and corrects for the measured radiation pattern. A handheld GPR system coupled with a metal detector is ready for routine use in mine fields. Recent advances in modeling, tomography, and full-waveform inversion, as well as Green’s function extraction through correlation and deconvolution, show much promise in this field.

A linear prediction land mine detection algorithm for hand held ground penetrating radar

Land mine detection using ground penetrating radar (GPR) is a difficult task because the background clutter characteristics are nonstationary and the land mine signatures are inconsistent. A particularly difficult scenario is the case for which a GPR is mounted on a hand held device with no position or velocity information available to a signal processing algorithm. This paper proposes the use of linear prediction in the frequency domain for land mine detection in this scenario. A frequency domain clutter vector sample is partitioned into subbands. Each subband is modeled by a linear prediction model; the current vector sample is expressed as a linear combination of the past few vector samples plus random noise. The detector first computes the maximum likelihood estimate of the prediction coefficients, and then uses the generalized likelihood method to determine if a land mine is present. The effect of subband processing on the accuracy of the detector is evaluated. Detection results are presented on data collected from a variety of geographical locations. The data sets contain over 2300 mine encounters of different size, shape and content, and a larger number of measurements from locations with no mines. The proposed detector is compared to the baseline differential energy detector. The proposed algorithm reduces the false alarm rate by 60% for all the targets at 90% probability of detection, and 70% for the deep anti-tank mines at 90% probability of detection.