A SAP-DOA Method for the Location of Two Buried Objects," International Journal on Antennas and Propagation (Special Issue on “Inverse Scattering and Microwave Tomography in Safety, Security, and Health”), vol. 2013, Article ID 702176, 10 pp., 2013; doi: 10.1155/2013/702176 (Italy; OPEN ACCESS)
Abstract: A localization technique for buried metallic and dielectric objects is proposed and tested. An array of isotropic antennas investigates a scenario with cylindrical targets buried in a dielectric soil. The targets are in the near field of the array, and a Sub-Array Processing (SAP) approach is adopted: the array is partitioned into subarrays, and Direction of Arrival (DoA) algorithms are used to process the electromagnetic field received by each subarray and estimate the dominant arrival direction of the signal. By triangulating all the estimated DoAs, a crossing pattern is obtained. It is filtered by a Poisson-based procedure and subsequently elaborated by the 𝑘-means clustering method in order to distinguish between targets and background, estimate the number of targets, and find their position. Several simulations have been performed to compare different DoA algorithms and to test the localization method in the presence of two buried cylinders. Different values of the permittivity of the involved dielectric materials have been considered; the target positions and size have also been varied. The proposed procedure can be useful for ground-penetrating radar applications, near-surface probing, and for the detection and localization of defects in a hosting medium.
Project 3.3 – Intrinsic models for describing near-field antenna effects (from the most recent paper to the oldest)
[wg3-p3-j2] A. De Coster, A. P. Tran, S. Lambot, "Fundamental Analyses on Layered Media Reconstruction Using GPR and Full-Wave Inversion in Near-Field Conditions," IEEE Transactions on Geoscience and Remote Sensing, vol. 54(9), pp. 5143-5158, September 2016; doi: 10.1109/TGRS.2016.2556862 (Belgium, United States; COOPERATION WITH IPC)
Abstract: An innovative information-acquisition approach to 2-D ground-penetrating radar (GPR) prospecting is presented. A microwave inverse-scattering nested scheme combining a frequency hopping (FH) procedure and a multifocusing (MF) technique is proposed. On the one hand, the FH scheme effectively handles multifrequency GPR data, whereas on the other hand, MF techniques have been proven to be effective tools in reducing the occurrence of multilocal minima affecting GPR investigations. This allows the use of a local search technique based on the conjugate gradient method to iteratively solve the inverse problem at hand. Selected results are reported and analyzed to give some insights to the interested readers on the advantages and limitations of such an approach when handling synthetically generated and experimental GPR data.
[wg3-p3-j1] F. André, S. Lambot, "Intrinsic Modeling of Near-Field Electromagnetic Induction Antennas for Layered Medium Characterization," IEEE Transactions on Geoscience and Remote Sensing, vol. 52(11), pp. 7457-7469, November 2014; doi: 10.1109/TGRS.2014.2312816 (Belgium)
Abstract: We present a closed-form equation for intrinsic modeling of near-field electromagnetic induction (EMI) antennas for planar layered media characterization. Resorting to a decomposition of the backscattered EM field into elementary distributions over the antenna aperture, the EMI transmitting and receiving antennas are modeled using infinitesimal magnetic dipoles and field points, and characteristic frequency-dependent global reflection and transmission coefficients. Low-frequency propagation of the EM fields in the medium is described using 3-D planar layered media Green's functions. We performed measurements with a loop antenna situated at different heights, ranging from near-field to far-field conditions, above water of known electrical conductivity to determine its intrinsic properties, and a range of salinity conditions was applied to subsequently validate the proposed model. The EMI system was set up using a vector network analyzer equipped with a prototype EMI antenna specifically designed for this application. The model showed good accuracy for reproducing the observed data, and model inversion provided good estimates of the medium electrical conductivity. Yet, insensitivity of the EMI signal to water electrical conductivity was encountered for low salinity due to the presence of a copper sheet as the bottom boundary condition of the experimental setup. Moreover, the efficiency of the antenna decreased rapidly as antenna height above water surface increases, leading to increasing discrepancies between estimated and measured water electrical conductivity values as the antenna moves away from the water surface. Although some technical improvements are still needed, the proposed approach is promising for quantitative estimation of soil electrical conductivity from EMI data.
Project 3.4 – Data processing for GPR (from the most recent paper to the oldest)
[wg3-p4-j7] F. Benedetto, F. Tosti, “A signal processing methodology for assessing the performance of ASTM standard test methods for GPR SYSTEMS,” Signal Processing (Elsevier), vol. 132, pp. 327–337, March 2017, doi: doi:10.1016/j.sigpro.2016.06.030 (Italy, United Kingdom; TU1208 SP Special Issue)
Abstract: Ground penetrating radar (GPR) is one of the most promising and effective non-destructive testing techniques (NDTs), particularly for the interpretation of the soil properties. Within the framework of international Agencies dealing with the standardization of NDTs, the American Society for Testing and Materials (ASTM) has published several standard test methods related to GPR, none of which is focused on a detailed analysis of the system performance, particularly in terms of precision and bias of the testing variable under consideration. This work proposes a GPR signal processing methodology, calibrated and validated on the basis of a consistent amount of data collected by means of laboratory-scale tests, to assess the performance of the above standard test methods for GPR systems. The (theoretical) expressions of the bias and variance of the estimation error are here investigated by a reduced Taylor's expansion up to the second order. Therefore, a closed form expression for theoretically tuning the optimal threshold according to a fixed target value of the GPR signal stability is proposed. Finally, the study is extended to GPR systems with different antenna frequencies to analyze the specific relationship between the frequency of investigation, the optimal thresholds, and the signal stability.
[wg3-p4-j6] A. Benedetto, F. Tosti, L. Bianchini Ciampoli, F. D’Amico, “An overview of ground-penetrating radar signal processing techniques for road inspections,” Signal Processing (Elsevier), vol. 132, pp. 201-209, March 2017, doi: 10.1016/j.sigpro.2016.05.016 (Italy, United Kingdom; TU1208 SP Special Issue)
Abstract: Ground-penetrating radar (GPR) was firstly used in traffic infrastructure surveys during the first half of the Seventies for testing in tunnel applications. From that time onwards, such non-destructive testing (NDT) technique has found exactly in the field of road engineering one of the application areas of major interest for its capability in performing accurate continuous profiles of pavement layers and detecting major causes of structural failure at traffic speed. This work provides an overview on the main signal processing techniques employed in road engineering, and theoretical insights and instructions on the proper use of the processing in relation to the quality of the data acquired and the purposes of the surveys.
[w3-p4-j5] N. Economou, "Time-varying band-pass filtering GPR data by self-inverse filtering," EAGE Near Surface Geophysics (NSG), vol. 14(2), pp. 207-217, April 2016, doi: 10.3997/1873-0604.2016015 (Greece; TU1208 NSG Special Issue)
Abstract: Even though ground penetrating radar data signal processing has already been studied by many researchers, more research is needed and expected from automatic ground penetrating radar data analysis. An automatic band-pass filtering procedure can lead to sufficient real-time data interpretation as signal buried in noise could be amplified. Ground penetrating radar traces are highly nonstationary, requiring time-varying processing techniques. An algorithm, based on self-inverse filtering, which is a special case of inverse filtering, was implemented. It is a ground penetrating radar trace filtering approach and is implemented by applying inverse filtering in each time sample in the time-frequency domain. Applied on a synthetic trace, this algorithm performed better than a stationary band-pass filter and empirical mode decomposition family methods, whereas its application on real ground penetrating radar data from two different sites enhanced reflections buried in noise without the need to test different high-frequency band stops and with minimum distortion of the signal and the initial temporal resolution of the data.
[wg3-p2-j4] J. Li, C. Le Bastard, Y. Wang, G. Wei, B. Ma, M. Sun, “Enhanced GPR Signal for Layered Media Time-Delay Estimation in Low-SNR Scenario,” IEEE Geoscience and Remote Sensing Letters, vol. 13(3), pp. 299-303, March 2016; doi: 10.1109/LGRS.2015.2502662 (France, China; COOPERATION WITH IPC)
Abstract: In this letter, a new method is proposed to enhance the ground-penetrating radar (GPR) signal for time-delay estimation in a low signal-to-noise ratio. It is based on a subspace method and a clustering technique. The proposed method makes it possible to improve the estimation accuracy in a noisy context. It is used with a compressive sensing method to estimate the time delay of layered media backscattered echoes coming from the GPR signal. Several simulations and an experiment are presented to show the effectiveness of signal enhancement.
[wg3-p4-j2] M. Varela-González, M. Solla, J. Martínez-Sánchez, P. Arias, "A semi-automatic processing and visualisation tool for ground-penetrating radar pavement thickness data," Automation in Construction (Elsevier), vol. 45, pp. 42-49, September 2014; doi: 10.1016/j.autcon.2014.05.004 (Spain)
Abstract: Ground-penetrating radar (GPR) is a recommendable and cost-effective non-destructive technique for measuring the thickness of pavement layers because data acquisition can take place at normal traffic speeds. On the other hand, the large amount of data collected is difficult to process. Given that processing is conducted by qualified practitioners, it is a key to obtain software tools that allow for accurate thickness measurements and fast processing times. This paper presents a new semi-automatic program for the processing and visualisation of GPR data to measure pavement thicknesses. The results showed that an optimisation in the execution time allowed for a near-immediate response in data processing even when dealing with large data sets. Different data set lengths, ranging from 100 m to 20 km, were analysed, and the processing times required to complete the entire process were examined taking into account three different hardware configurations (i3, i5 and i7 processors). In all cases, the processing times did not exceed 30 s. An additional test was performed to evaluate the reproducibility of the algorithm on a well-defined and preconditioned concrete asphalt course. Furthermore, the visualisation application allows for the georeferencing of the field GPR data by using additional GPS data.
[wg3-p4-j1] M. Manataki, A. Vafidis, A. Sarris, "Application of empirical mode decomposition methods to ground penetrating radar data," First Break (EAGE), vol. 32, pp. 67-71, August 2014 (Greece)
Abstract: Empirical Mode Decomposition (EMD) is a relatively new technique introduced by Huang et al., (1998) for analysing non-linear and non-stationary time series. The decomposition is based on a signal’s local extrema, which define different oscillation modes present in the signal. What EMD does is the separation of those differ- ent oscillatory modes into a finite and usually small number of stationary sub-signals called Instrinsic Mode Functions (IMFs). EMD suffers from mode mixing which limits the frequency separation among the different modes and makes the physical meaning of the IMFs unclear.
The introduction of Ensemble Empirical Mode Decomposition (EEMD) by Wu and Huang (2009) as an EMD-based noise assistance method improved the modes separation by eliminating the mode-mixing problem. The signal is not fully reconstructed by the IMFs calculated from EEMD. The latter lead Torres et al. (2011) to propose another modification, the Complete Ensemble Empirical Mode Decomposition (CEEMD). CEEMD is also a noise assisted and adaptive method where the original signal can be fully reconstructed. EMD-based algorithms have been applied to seismic reflection data (Battista et al., 2007; Bekara and Van der Baan, 2009). Battista et al. (2009) used EMD to remove wow noise from Ground Penetrating Radar (GPR) data. Chen and Jeng, (2011), applied EEMD to enhance GPR data and provided promising results. Here, we compare the empirical mode decomposition methods using both synthetic and real GPR data. In particular, we examine: (1) the separation of high-frequency wavelets from the low frequency ones and (2) the noise level that yields better decomposition for EEMD and CEEMD. We also examined the capability of these decomposition methods to remove random and coherent noise from real GPR data.
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