Ground Penetrating Radar Sensors Research Articles

A Study on Drone Based Mine Detection and Diffusion System: Present Scenario and Potential Prospects

Abstract: Decades-long issues are caused by explosive remnants of war (ERW) and landmines that remain subterranean during times of conflict. Therefore, it is critical to promptly identify concealed explosive devices and secure the former zones of conflict. The sensor is affixed to the drone via a stabilization robot that possesses a bouncing motion that enables the detector to precisely survey the ground.. After knowing location of mines, its getting diffuser destroy by using drones .Due to drones mines detection is getting easy than manually or mobile vehicle tracking. Here we are focusing on GPR (Ground Penetrating Radar) and IR(Infrared)sensor for detecting land mines below the ground also we using a camera to know the precise location of mine. For defusing mine dead-weight is used. Dead-weight is thrown from drone to destroy mines. By using drones it is convenient to cover more area in less time. This research aims to produce a technical reference that increases awareness and understanding of unmanned aerial vehicle (UAV) monitoring in Massachusetts, in addition to evaluating its environmental, mining, and reclamation applications.

Using Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) to monitor the effects of seasonal moisture variation on the structural capacity of pavements

Moisture fluctuations in pavement foundation due to environmental conditions (e.g., heavy precipitation, freeze–thaw cycles, groundwater table variation, etc.) can significantly affect pavements’ short- and long-term performance. Moisture variation in the pavement foundation is often monitored as a way to anticipate the structural capacity of pavements. Thus, the ability to monitor moisture variation through a proven non-destructive technology (NDT) such as Ground Penetrating Radar (GPR) would be beneficial for asset management by transportation agencies. This paper summarizes the Minnesota Department of Transportation’s (MnDOT) efforts at validating the use of GPR to monitor moisture in the pavement foundation through comparison with Falling-Weight Deflectometer (FWD) parameters that represent the structural condition of the pavement. The results in this paper are based on GPR, FWD and in-place moisture sensor data collected over 17 months on MnROAD instrumented test sections. The pavement test sections considered in this study included unbound aggregate bases (UAB) with virgin and recycled materials (i.e., RCA and RAP) covering a broad range of geotechnical behavior. Previous efforts established a strong correlation between GPR-based moisture measurements and sensor-based moisture measurements. This correlation is further validated by investigating the relationship between GPR-based moisture measurements and FWD-based indices for structural capacity. The observed correlation is reasonable and in good agreement with the expected correlation between volumetric moisture content (VMC) of the base layer and the structural capacity of a pavement. This agreement validates the use of GPR to monitor moisture in pavement foundation. GPR is not a replacement for FWD, but it can be used in asset management efforts in addition to FWD to assess moisture fluctuation in pavement foundation during and after extreme environmental events (e.g., flooding, freeze-thawing) to determine when and where FWD testing may be necessary.

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

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.

Characterization of Electromagnetic Properties of In Situ Soils for the Design of Landmine Detection Sensors: Application in Donbass, Ukraine

To design holographic and impulse ground penetrating radar (GPR) sensors suitable for humanitarian de-mining in the Donbass (Ukraine) conflict zone, we measured critical electromagnetic parameters of typical local soils using simple methods that could be adapted to any geologic setting. Measurements were recorded along six profiles, each crossing at least two mapped soil types. The parameters selected to evaluate GPR and metal detector sensor performance were magnetic permeability, electrical conductivity, and dielectric permittivity. Magnetic permeability measurements indicated that local soils would be conducive to metal detector performance. Electrical conductivity measurements indicated that local soils would be medium to high loss materials for GPR. Calculation of the expected attenuation as a function of signal frequency suggested that 1 GHz may have optimized the trade-off between resolution and penetration and matched the impulse GPR system power budget. Dielectric permittivity was measured using both time domain reflectometry and impulse GPR. For the latter, a calibration procedure based on an in-situ measurement of reflection coefficient was proposed and the data were analyzed to show that soil conditions were suitable for the reliable use of impulse GPR. A distinct difference between the results of these two suggested a dry (low dielectric) soil surface, grading downward into more moist (higher dielectric) soils. This gradation may provide a matching layer to reduce ground surface reflections that often obscure shallow subsurface targets. In addition, the relatively high dielectric deeper (10 cm–20 cm) subsurface soils should provide a strong contrast with plastic-cased mines.

GPR Image Enhancement Based on Frequency Shifting and Histogram Dissimilarity

In the ground-penetrating radar (GPR) B-scan images, various noise sources are superimposed due to the ruggedness of the surface, sensor vibration, and multiple reflections. Additionally, the intensity of the received signals from small-size or low-metal-content landmines is low. Thus, it is difficult to accurately detect buried mines. In this letter, we propose an effective method to improve the B-scan image so that accurate landmine detection is possible even in such conditions. The proposed B-scan image enhancement method is comprised of two main techniques: an A-scan transformation based on frequency shifting and a background-landmine dissimilarity measurement using cumulative intensity distribution (CID). Based on frequency shifting, an A-scan transformation is devised to attenuate the strong ac component contained by the received A-scan signal. The CID-based dissimilarity is introduced to measure how an A-scan differs from a background model in the presence of a landmine. The proposed dissimilarity measure provides a robust response to a rugged ground surface and sensor vibration. For performance analysis, we compared our method with some conventional methods using the GPR data set acquired by an ultra wide band GPR sensor manufactured by Hanwha Systems Co., Ltd. We carried out various experiments and verified that the proposed method has a better performance than the conventional methods.

Drone-borne GPR design: Propagation issues

Abstract In this paper, we shall address the electromagnetic wave propagation issues that are critical to determining the feasibility of a drone-borne ground-penetrating radar sensor for humanitarian applications, particularly in the context of disaster management. Frequency- and polarization-dependent scattering, attenuation and dispersion of radar signals penetrating into the sub-surface region will determine the applicability of a drone-mounted radar sensor capable of registering radar echoes for observing and monitoring sub-surface features. The functionality of the radar will thus be assessed depending on key radar parameters that include the central radar frequency, the modulation depth, and the mode of radar operation (pulsed FM, FM-CW), the antenna type, the available power-budget. In the analysis to be presented, the radar equation, together with the aforementioned propagation effects, will be used to simulate the signal strength of radar echoes under different conditions arising from the chosen key-radar parameters and the assumed physical properties of the sub-surface earth medium. The analysis to be presented will indicate whether or not the drone-borne ground-penetrating radar is a feasible system and if it could be constructed with the technologies available today. Taking into account the strict constraints involved to design drone applications for Public Protection and Disaster Relief (PPDR), the ideas developed hereafter are both prospective and exploratory. The objective is to see if a solution can be found in the near future.

Ground Penetrating Radar as a Contextual Sensor for Multi-Sensor Radiological Characterisation.

Radioactive sources exist in environments or contexts that influence how they are detected and localised. For instance, the context of a moving source is different from a stationary source because of the effects of motion. The need to incorporate this contextual information in the radiation detection and localisation process has necessitated the integration of radiological and contextual sensors. The benefits of the successful integration of both types of sensors is well known and widely reported in fields such as medical imaging. However, the integration of both types of sensors has also led to innovative solutions to challenges in characterising radioactive sources in non-medical applications. This paper presents a review of such recent applications. It also identifies that these applications mostly use visual sensors as contextual sensors for characterising radiation sources. However, visual sensors cannot retrieve contextual information about radioactive wastes located in opaque environments encountered at nuclear sites, e.g., underground contamination. Consequently, this paper also examines ground-penetrating radar (GPR) as a contextual sensor for characterising this category of wastes and proposes several ways of integrating data from GPR and radiological sensors. Finally, it demonstrates combined GPR and radiation imaging for three-dimensional localisation of contamination in underground pipes using radiation transport and GPR simulations.

Automated GPR Rebar Analysis for Robotic Bridge Deck Evaluation.

Ground penetrating radar (GPR) is used to evaluate deterioration of reinforced concrete bridge decks based on measuring signal attenuation from embedded rebar. The existing methods for obtaining deterioration maps from GPR data often require manual interaction and offsite processing. In this paper, a novel algorithm is presented for automated rebar detection and analysis. We test the process with comprehensive measurements obtained using a novel state-of-the-art robotic bridge inspection system equipped with GPR sensors. The algorithm achieves robust performance by integrating machine learning classification using image-based gradient features and robust curve fitting of the rebar hyperbolic signature. The approach avoids edge detection, thresholding, and template matching that require manual tuning and are known to perform poorly in the presence of noise and outliers. The detected hyperbolic signatures of rebars within the bridge deck are used to generate deterioration maps of the bridge deck. The results of the rebar region detector are compared quantitatively with several methods of image-based classification and a significant performance advantage is demonstrated. High rates of accuracy are reported on real data that includes thousands of individual hyperbolic rebar signatures from three real bridge decks.

Combining multi‐receiver electromagnetic induction and stepped frequency ground penetrating radar for industrial site investigation

SummaryThe soil at industrial sites is frequently characterized by very heterogeneous properties, which are often related to physical disturbance and contamination. A conventional approach to characterize the soil, with only a limited number of invasive observations, fails to capture the full extent of soil heterogeneity. Proximal soil sensing provides efficient tools to record spatially dense soil information. Nevertheless, because the output of most sensors is affected by more than one soil property, the simultaneous characterization of different soil properties requires the use of multiple sensors. Here, we apply multi‐receiver electromagnetic induction (EMI) and stepped frequency ground penetrating radar (GPR) to survey a former gasworks site in a seaport area of Belgium. We used the EMI and GPR sensors in a motorized system to obtain densely sampled measurements of apparent electrical conductivity, apparent magnetic susceptibility and contrasts in relative dielectric permittivity. Our study shows that the sensors give detailed information on the variation in these electromagnetic soil properties. Interpretation of the variation in terms of the stratification of the soil was hampered by localized anthropogenic disturbances. However, the sensors provided complementary information that enabled the identification, discrimination and accurate location of several of these localized disturbances, including underground utility services such as electric cables, buried structures such as the remains of foundations and contamination by salts. Because these represent typical targets in industrial site investigation, we conclude that multi‐receiver EMI and stepped frequency GPR provide a useful set of tools to expedite the investigation of industrial sites.

Wideband Antenna With Conductive Textile Radiators for a Dual-Sensor Subsurface Detection System

A wideband antenna with conductive textile radiators (WACTR) is investigated for a pulsed dual-sensor subsurface detection system, which is composed of a ground penetrating radar (GPR) sensor and an electromagnetic induction (EI) sensor. It is demonstrated that undesired clutter of the EI sensor arising from the metallic mass and conductivity of the GPR antenna radiators can be removed by choosing textile material for the antenna without degradation of the antenna's performance. To the best of our knowledge, this is the first application of textile material used in antennas for such an aim. The antenna also has a new feeding structure to excite the textile patches, which offers a mechanically more rigid solution and a wider excitation surface. Some measured and simulated antenna characteristics are presented for the antenna located in the dual-sensor search head (DSSH). The ground effects on the antenna performance are additionally investigated as a complementary study. The detection results of some selected targets buried in soil by means of the proposed WACTR are presented. The targets are detected even in the non-metallic and deep-buried cases and it is also demonstrated that the GPR and EI sensors are complementary sensors.

A Large-Scale Systematic Evaluation of Algorithms Using Ground-Penetrating Radar for Landmine Detection and Discrimination

A variety of algorithms for the detection of landmines and discrimination between landmines and clutter objects have been presented. We discuss four quite different approaches in using data collected by a vehicle-mounted ground-penetrating radar sensor to detect landmines and distinguish them from clutter objects. One uses edge features in a hidden Markov model; the second uses geometric features in a feed-forward order-weighted average network; the third employs spectral features as its basis; and the fourth clusters edge histograms. We present the results of a large-scale cross-validation evaluation that uses a diverse set of data collected over 41 807.57 m2 of ground, including 1593 mine encounters. Finally, we discuss the results of that ranking and what one can conclude concerning the performance of these four algorithms in various settings.

Accuracy of Ground Penetrating Radar Horn-Antenna Technique for Sensing Pavement Subsurface

Ground penetrating radar (GPR) is an intelligent sensor technique that has led to a powerful nondestructive testing (NDT) method for road pavement evaluation. Recent improvements in hardware and, in particular, software processing have contributed to the rapidly expanding popularity and usability of this technique in the pavement engineers community. In the present work, a GPR sensor system mounted on a driving van was used to acquire data from an asphalt pavement. Two different central frequency air-coupled horn antennas were used for data acquisition. The collected radar data was processed and analyzed in order for the accuracy of the antennas to be evaluated. For this purpose, the asphalt layer dielectric values and related thickness values were also estimated. Based on the GPR data analysis results, the performance of the two horn antennas in terms of sensing pavement subsurface is presented and discussed in the paper

Land Mine and Clutter Object Discrimination Using Wavelet and Time Domain Spatially Distributed Features from Metal Detectors and Their Fusion with GPR Features for Hand-Held Units

This paper presents some advances in discrimination and fusion algorithms using metal detector (MD) and ground penetrating radar (GPR) sensors in a robotic wand unit. Previously investigated spatially distributed features are extended and fused with discrete wavelet transform representations of MD data. A multilayer perceptron technique is then applied to discriminate between land mine and land mine-like objects based on the wavelet coefficient and time domain features separately. Using MD wavelet and time domain fusion, the probability of false alarms is reduced by 46.0% and 18.0% over the wavelet and time domain models, respectively, at 0.95 probability of detection. Fusion results are presented for the MD and GPR sensors to demonstrate that the two sensors provide complementary information for significantly reducing the probability of false alarm. Blind test results from a government test facility are presented to evaluate the effectiveness of the algorithms.

Application of feature extraction methods for landmine detection using the Wichmann/Niitek ground-penetrating radar

Ground-penetrating radar (GPR) has been proposed as an alternative to classical electromagnetic induction techniques for the landmine detection problem. The Wichmann/Niitek system provides a good platform for novel GPR-based antitank mine detection and classification algorithm development due to its extremely high SNR. When the GPR sensor is mounted on a moving vehicle, the target signatures are hyperbolas in a time-domain data record. The goal of this work is to extract useful features that exploit this knowledge in order to improve target detection. The algorithms can be divided into two steps: feature extraction and classification. Preprocessing is also considered to remove both stationary effects and nonstationary drift of the data and to improve the contrast of the desired hyperbolas. The algorithm is evaluated using real data over primarily plastic antitank mines collected with a fielded GPR sensor at a government test site.

Detecting landmines with ground-penetrating radar using feature-based rules, order statistics, and adaptive whitening

An approach to detecting landmines using ground-penetrating radar (GPR) based on feature-based rules, order statistics, and adaptive whitening (FROSAW) is described. FROSAW relies on independent adaptation of whitening statistics in different depths and combining feature-based methods with anomaly detection using rules. Constant false alarm rate (CFAR) detectors are used for anomaly detection on the depth-dependent adaptively whitened data. A single CFAR confidence measure is computed via a function of order statistics. Anomalies are detected at locations with high CFAR confidence. Depth-dependent features are computed on regions containing anomalies. Rules based on the features are used to reject alarms that do not exhibit mine-like properties. The utility of combining the CFAR and feature-based methods is evaluated. The algorithms and analysis are applied to data acquired from tens of thousands of square meters from several outdoor test sites with a state-of-the-art array of GPR sensors.

Feature and decision level sensor fusion of electromagnetic induction and ground penetrating radar sensors for landmine detection with hand-held units

Abstract Strategies for fusion of electromagnetic induction (metal detector (MD)) and ground penetrating radar (GPR) sensors for landmine detection are investigated. Feature and decision level algorithms are devised and compared. Features are extracted from the MD signals by correlating with weighted density distribution functions. A multi-frequency band linear prediction method generates features for the GPR. Feature level fusion combines MD and GPR features in a single neural network. Decision level fusion is performed by using the MD features as inputs to one neural network and the GPR features as inputs to the geometric mean and combining the output values. Experimental results are reported on a very large real data set containing 2315 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions at three distinct geographical locations.