SPOT-GPR: a freeware tool for target detection and localizationin GPR data developed within the COST action TU1208

SPOT-GPR (release 1.0) is a new freeware tool implementing an innovative Sub-Array Processing method, for the analysis of Ground-Penetrating Radar (GPR) data with the main purposes of detecting and localizing targets. The software is implemented in Matlab, it has a graphical user interface and a short manual. This work is the outcome of a series of three Short-Term Scientific Missions (STSMs) funded by European COoperation in Science and Technology (COST) and carried out in the framework of the COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar” (www.GPRadar.eu). The input of the software is a GPR radargram (B-scan). The radargram is partitioned in subradargrams, composed of a few traces (A-scans) each. The multi-frequency information enclosed in each trace is exploited and a set of dominant Directions of Arrival (DoA) of the electromagnetic field is calculated for each sub-radargram. The estimated angles are triangulated, obtaining a pattern of crossings that are condensed around target locations. Such pattern is filtered, in order to remove a noisy background of unwanted crossings, and is then processed by applying a statistical procedure. Finally, the targets are detected and their positions are predicted. For DoA estimation, the MUltiple SIgnal Classification (MUSIC) algorithm is employed, in combination with the matched filter technique. To the best of our knowledge, this is the first time the matched filter technique is used for the processing of GPR data. The software has been tested on GPR synthetic radargrams, calculated by using the finite-difference timedomain simulator gprMax, with very good results

A practical guide on using SPOT-GPR, a freeware tool implementing a SAP-DoA technique

This is a software paper, which main objective is to provide practical information on how to use SPOT-GPR release 1.0, a MATLAB®-based software for the analysis of ground penetrating radar (GPR) profiles. The software allows detecting targets and estimating their position in a two-dimensional scenario, it has a graphical user interface and implements an innovative sub-array processing method. SPOT-GPR was developed in the framework of the COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar” and is available for free download on the website of the Action (www.GPRadar.eu)

Application of coupled-wave Wentzel-Kramers-Brillouin approximation to ground penetrating radar

This paper deals with bistatic subsurface probing of a horizontally layered dielectric half-space by means of ultra-wideband electromagnetic waves. In particular, the main objective of this work is to present a new method for the solution of the two-dimensional back-scattering problem arising when a pulsed electromagnetic signal impinges on a non-uniform dielectric half-space; this scenario is of interest for ground penetrating radar (GPR) applications. For the analytical description of the signal generated by the interaction of the emitted pulse with the environment, we developed and implemented a novel time-domain version of the coupled-wave Wentzel-Kramers-Brillouin approximation. We compared our solution with finite-difference time-domain (FDTD) results, achieving a very good agreement. We then applied the proposed technique to two case studies: in particular, our method was employed for the post-processing of experimental radargrams collected on Lake Chebarkul, in Russia, and for the simulation of GPR probing of the Moon surface, to detect smooth gradients of the dielectric permittivity in lunar regolith. The main conclusions resulting from our study are that our semi-analytical method is accurate, radically accelerates calculations compared to simpler mathematical formulations with a mostly numerical nature (such as the FDTD technique), and can be effectively used to aid the interpretation of GPR data. The method is capable to correctly predict the protracted return signals originated by smooth transition layers of the subsurface dielectric medium. The accuracy and numerical efficiency of our computational approach make promising its further development

Automatic sparse esm scan using Gaussian process regression

“Emission source microscopy (ESM) technique can be utilized for localization of electromagnetic interference sources in complex and large systems. In this work a Gaussian process regression (GPR) method is applied in real-time to select sampling points for the sparse ESM imaging using a motorized scanner. The Gaussian process regression is used to estimate the complex amplitude of the scanned field and its uncertainty allowing to select the most relevant areas for scanning. Compared with the randomly selected samples the proposed method allows to reduce the number of samples needed to achieve a certain dynamic range of the image, reducing the overall scanning time. Results for simulated and measured 1D scans are presented. This method allowed to reduce the number of samples needed to achieve a certain dynamic range of the image, reducing the overall scanning time and eliminating a need of human intervention into the ESM process. Based on the work of 1D scan and Gaussian Regression Sparse ESM strategy, the second work in this paper extends the application of the ESM with GPR sampling to 2D scenes with multiple sources, including distributed ones. The automatic GPR ESM method can intelligently and automatically control the scanning process, reducing the number of measurement points with less image quality degradation compared to the random ESM scanning”--Abstract, page iv

A GPR-GPS-GIS-integrated, information-rich and error-aware system for detecting, locating and characterizing underground utilities

Underground utilities have proliferated throughout the years. The location and dimension of many underground utilities have not always been properly collected and documented, leading to utility conflicts and utility strikes, and thus resulting in property damages, project delays, cost overruns, environment pollutions, injuries and deaths. The underlying reasons are twofold. First, the reliable data regarding the location and dimension of underground utility are missing or incomplete. Existing methods to collect data are not efficient and effective. Second, positional uncertainties are inherent in the measured utility locations. An effective means is not yet available to visualize and communicate the inherent positional uncertainties associated with utility location data to end-users (e.g., excavator operator). To address the aforementioned problems, this research integrate ground penetrating radar (GPR), global positioning system (GPS) and geographic information system (GIS) to form a total 3G system to collect, inventory and visualize underground utility data. Furthermore, a 3D probabilistic error band is created to model and visualize the inherent positional uncertainties in utility data. ^ Three main challenges are addressed in this research. The first challenge is the interpretation of GPR and GPS raw data. A novel method is created in this research to simultaneously estimate the radius and buried depth of underground utilities using GPR scans and auxiliary GPS data. The proposed method was validated using GPR field scans obtained under various settings. It was found that this newly created method increases the accuracy of estimating the buried depth and radius of the buried utility under a general scanning condition. The second challenge is the geo-registration of detected utility locations. This challenge is addressed by integration of GPR, GPS and GIS. The newly created system takes advantages of GPR and GPS to detect and locate underground utilities in 3D and uses GIS for storing, updating, modeling, and visualizing collected utility data in a real world coordinate system. The third challenge is positional error/uncertainty assessment and modeling. The locational errors of GPR system are evaluated in different depth and soil conditions. Quantitative linkages between error magnitudes and its influencing factors (i.e., buried depths and soil conditions) are established. In order to handle the positional error of underground utilities, a prototype of 3D probabilistic error band is created and implemented in GIS environment. This makes the system error-aware and also paves the way to a more intelligent error-aware GIS. ^ To sum up, the newly created system is able to detect, locate and characterize underground utilities in an information-rich and error-aware manner

Railway track condition assessment at network level by frequency domain analysis of GPR data

The railway track system is a crucial infrastructure for the transportation of people and goods in modern societies. With the increase in railway traffic, the availability of the track for monitoring and maintenance purposes is becoming significantly reduced. Therefore, continuous non-destructive monitoring tools for track diagnoses take on even greater importance. In this context, Ground Penetrating Radar (GPR) technique results yield valuable information on track condition, mainly in the identification of the degradation of its physical and mechanical characteristics caused by subsurface malfunctions. Nevertheless, the application of GPR to assess the ballast condition is a challenging task because the material electromagnetic properties are sensitive to both the ballast grading and water content. This work presents a novel approach, fast and practical for surveying and analysing long sections of transport infrastructure, based mainly on expedite frequency domain analysis of the GPR signal. Examples are presented with the identification of track events, ballast interventions and potential locations of malfunctions. The approach, developed to identify changes in the track infrastructure, allows for a user-friendly visualisation of the track condition, even for GPR non-professionals such as railways engineers, and may further be used to correlate with track geometric parameters. It aims to automatically detect sudden variations in the GPR signals, obtained with successive surveys over long stretches of railway lines, thus providing valuable information in asset management activities of infrastructure managers

Landmine Detection From Gpr Data Using Convolutional Neural Networks

The presence of buried landmines is a serious threat in many areas around the World. Despite various techniques have been proposed in the literature to detect and recognize buried objects, automatic and easy to use systems providing accurate performance are still under research. Given the incredible results achieved by deep learning in many detection tasks, in this paper we propose a pipeline for buried landmine detection based on convolutional neural networks (CNNs) applied to ground-penetrating radar (GPR) images. The proposed algorithm is capable of recognizing whether a B-scan profile obtained from GPR acquisitions contains traces of buried mines. Validation of the presented system is carried out on real GPR acquisitions, albeit system training can be performed simply relying on synthetically generated data. Results show that it is possible to reach 95% of detection accuracy without training in real acquisition of landmine profiles. Document type: Conference objec

Autonomous Robotic System using Non-Destructive Evaluation methods for Bridge Deck Inspection

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments

THE 4DILAN PROJECT (4TH DIMENSION IN LANDSCAPE AND ARTIFACTS ANALYSES)

The project is part of the wider application and subsequent spread of innovative digital technologies involving robotic systems. Modern society needs knowledge and investigation of the environment and of the related built landscape; therefore it increasingly requires new types of information. The goal can be achieved through the innovative integration of methods to set new analysis strategies for the knowledge of the built heritage and cultural landscape. The experimental cooperation between different disciplines and the related tools and techniques, which this work suggests for the analysis of the architectural heritage and the historical territory, are the following: - 3D metric survey techniques with active and passive sensors - the latter operating in both terrestrial mode and by aerial point of view. In some circumstances, beyond the use of terrestrial LiDAR, even the newest mobile mapping system using SLAM technology (simultaneous localization and mapping) has been tested. - Techniques of non-destructive investigation, such as geophysical analysis of the subsoil and built structures, in particular GPR (Ground Penetrating Radar) techniques. - Historic and stratigraphic surveys carried out primarily through the study and interpretation of documentary sources, cartography and historical iconography, closely related to the existing data or latent material. The experience through the application of these techniques of investigation connected to the built spaces and to the manmade environments has been achieved with the aim of improving the ability to analyse the occurred transformations/layers over time and no longer directly readable or interpretable on manufactured evidence