Detecting Improvised Explosive Devices Via Forward Looking Ground Penetrating Radar

Forward-looking ground penetrating radar shows promise for detection of improvised explosive devices in active war zones. Because of certain insurmountable physical limitations, post-processing algorithm development is the most popular research topic in this field. One such investigative avenue explores the worthiness of frequency analysis during data post-processing. Using the finite difference time domain numerical method, simulations are run to test both mine and clutter frequency response. Mines are found to respond strongest at low frequencies and cause periodic changes in ground penetrating radar frequency results. These results are called into question, however, when clutter, a phenomenon generally known to be random, is also found to cause periodic frequency effects. Possible causes, including simulation inaccuracy, are considered. Although the clutter models used are found to be inadequately random, specular reflections of differing periodicity are found to return from both the mine and the ground. The presence of these specular reflections offers a potential alternative method of determining a mine’s presence

Electromagnetic modelling and simulation of a high-frequency ground penetrating radar antenna over a concrete cell with steel rods

This work focuses on the electromagnetic modelling and simulation of a highfrequency Ground-Penetrating Radar (GPR) antenna over a concrete cell with reinforcing elements. The development of realistic electromagnetic models of GPR antennas is crucial for accurately predicting GPR responses and for designing new antennas. We used commercial software implementing the Finite-Integration technique (CST Microwave Studio) to create a model that is representative of a 1.5 GHz Geophysical Survey Systems, Inc. antenna, by exploiting information published in the literature (namely, in the PhD Thesis of Dr Craig Warren); our CST model was validated, in a previous work, by comparisons with FiniteDifference Time-Domain results and with experimental data, with very good agreement, showing that the software we used is suitable for the simulation of antennas in the presence of targets in the near field. In the current paper, we firstly describe in detail how the CST model of the antenna was implemented; subsequently, we present new results calculated with the antenna over a reinforced-concrete cell. Such cell is one of the reference scenarios included in the Open Database of Radargrams of COST Action TU1208 “Civil engineering applications of Ground Penetrating Radar” and hosts five circular-section steel rods, having different diameters, embedded at different depths into the concrete. Comparisons with a simpler model, where the physical structure of the antenna is not taken into account, are carried out; the significant differences between the results of the realistic model and the results of the simplified model confirm the importance of including accurate models of the actual antennas in GPR simulations; they also emphasize how salient it is to remove antenna effects as a pre-processing step of experimental GPR data. The simulation results of the antenna over the concrete cell presented in this paper are attached to the paper as ‘Supplementary materials.

Realistic FDTD GPR antenna models optimized using a novel linear/nonlinear Full-Waveform Inversion

Finite-Difference Time-Domain (FDTD) modelling of Ground Penetrating Radar (GPR) is becoming regularly used in model-based interpretation methods like full waveform inversion (FWI), and machine learning schemes using synthetic training data. Oversimplifications in such forward models can compromise the accuracy and realism with which real GPR responses can be simulated, and this degrades the overall performance of the aforementioned interpretation techniques. Therefore, a forward model must be able to accurately simulate every part of the GPR problem that can affect the resulting scattered field. A key element is the antenna system and excitation waveform, so the model must contain a complete description of the antenna including the excitation source and waveform, the geometry, and the dielectric properties of materials in the antenna. The challenge is that some of these parameters are not known or easily measured, especially for commercial GPR antennas that are used in practice. We present a novel hybrid linear/non-linear FWI approach which can be used, with only knowledge of the basic antenna geometry, to simultaneously optimise the dielectric properties and excitation waveform of the antenna, and minimise the error between real and synthetic data. The accuracy and stability of our proposed methodology is demonstrated by successfully modelling a Geophysical Survey Systems (GSSI) Inc. 1.5~GHz commercial antenna. Our framework allows accurate models of GPR antennas to be developed without requiring detailed knowledge of every component in the antenna. This is significant because it allows commercial GPR antennas, regularly used in GPR surveys, to be more readily simulated

A Realistic FDTD Numerical Modeling Framework of Ground Penetrating Radar for Landmine Detection

A three-dimensional (3-D) finite-difference time-domain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical and laboratory measurements. The modeled AP landmines are buried in a realistically simulated soil. The geometrical characteristics of soil's inhomogeneity are modeled using fractal correlated noise, which gives rise to Gaussian semivariograms often encountered in the field. Fractals are also employed in order to simulate the roughness of the soil's surface. A frequency-dependent complex electrical permittivity model is used for the dielectric properties of the soil, which relates both the velocity and the attenuation of the electromagnetic waves with the soil's bulk density, sand particles density, clay fraction, sand fraction, and volumetric water fraction. Debye functions are employed to simulate this complex electrical permittivity. Background features like vegetation and water puddles are also included in the models and it is shown that they can affect the performance of GPR at frequencies used for landmine detection (0.5-3 GHz). It is envisaged that this modeling framework would be useful as a testbed for developing novel GPR signal processing and interpretations procedures and some preliminary results from using it in such a way are presented

Optimising the complex refractive index model for estimating the permittivity of heterogeneous concrete models

Peer reviewedPublisher PD

TU1208 open database of radargrams. the dataset of the IFSTTAR geophysical test site

This paper aims to present a wide dataset of ground penetrating radar (GPR) profiles recorded on a full-size geophysical test site, in Nantes (France). The geophysical test site was conceived to reproduce objects and obstacles commonly met in the urban subsurface, in a completely controlled environment; since the design phase, the site was especially adapted to the context of radar-based techniques. After a detailed description of the test site and its building process, the GPR profiles included in the dataset are presented and commented on. Overall, 67 profiles were recorded along eleven parallel lines crossing the test site in the transverse direction; three pulsed radar systems were used to perform the measurements, manufactured by different producers and equipped with various antennas having central frequencies from 200 MHz to 900 MHz. An archive containing all profiles (raw data) is enclosed to this paper as supplementary material. This dataset is the core part of the Open Database of Radargrams initiative of COST (European Cooperation in Science and Technology) Action TU1208 “Civil engineering applications of Ground Penetrating Radar”. The idea beyond such initiative is to share with the scientific community a selection of interesting and reliable GPR responses, to enable an effective benchmark for direct and inverse electromagnetic approaches, imaging methods and signal processing algorithms. We hope that the dataset presented in this paper will be enriched by the contributions of further users in the future, who will visit the test site and acquire new data with their GPR systems. Moreover, we hope that the dataset will be made alive by researchers who will perform advanced analyses of the profiles, measure the electromagnetic characteristics of the host materials, contribute with synthetic radargrams obtained by modeling the site with electromagnetic simulators, and more in general share results achieved by applying their techniques on the available profiles

Fractal-Constrained Crosshole/Borehole-to-Surface Full Waveform Inversion for Hydrogeological Applications Using Ground-Penetrating Radar

Peer reviewedPostprin

A Machine Learning Approach For Simulating Ground Penetrating Radar

The ability to produce, store and analyse large amounts of well-labeled data as well as recent advancements on supervised training, led machine learning to gain a renewed popularity. In the present paper, the applicability of machine learning to simulate ground penetrating radar (GPR) for high frequency applications is examined. A well-labelled and equally distributed training set is generated synthetically using the finite difference time-domain (FDTD) method. Special care was taken in order to model the antennas and the soils with sufficient accuracy. Through a stochastic parameterisation, each model is expressed using only seven parameters (i.e. the fractal dimension of water fraction, the heigh of the antenna and so on). Based on these parameters and the synthetically generated training set, a machine learning framework is trained to predict the resulting A-Scan in real-time. Thus, overcoming the time-consuming calculations required for an equivalent FDTD simulation