216 research outputs found
GPR Clutter Reflection Noise-Filtering through Singular Value Decomposition in the Bidimensional Spectral Domain
Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between
the useful signal and the noise through standard filtering to isolate the effective signal as much as
possible. However, there are true reflections that mask the coherent reflectors that can be considered
noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface
elements (e.g., isolated masonry, ceramic objects, and archaeological collapses). Its elimination is
difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating
artefacts. In this study, a procedure to filter the clutter reflection noise (CRN) from GPR datasets is
presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D
spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory
environment, to establish a mathematical control of this algorithm. Additionally, it has been applied
in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is
an uncontrolled environment. The results show an increase in the quality of archaeological GPR
planimetry that was verified via archaeological excavation.Project “Innovación abierta e
inteligente en la EUROACE” 0049_INNOACE_4_E - European Union (European Regional Development Fund) COMPETE 2020Portuguese Foundation for Science and Technology (FCT) projects UIDB/04683/2020-ICT (Institute
of Earth Sciences) and SFRH/BSAB/143063/201
Advanced Techniques for Ground Penetrating Radar Imaging
Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives
GPR Clutter Reflection Noise-Filtering through Singular Value Decomposition in the Bidimensional Spectral Domain
Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects, and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artefacts. In this study, a procedure to filter the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Additionally, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological GPR planimetry that was verified via archaeological excavation
Clutter removal of near-field UWB SAR imaging for pipeline penetrating radar
Recently, ultrawideband (UWB) near-field synthetic aperture radar (SAR) imaging has been proposed for pipeline penetrating radar applications thanks to its capability in providing suitable resolution and penetration depth. Because of geometrical restrictions, there are many complicated sources of clutter in the pipe. However, this issue has not been investigated yet. In this article, we investigate some well-known clutter removal algorithms
using full-wave simulated data and compare their results considering
image quality, signal to clutter ratio and contrast. Among candidate algorithms, two-dimensional singular spectrum analysis (2-D SSA) shows a good potential to improve the signal to clutter ratio. However, basic 2-D SSA produces some artifacts in the image. Therefore, to mitigate this issue, we propose “modified 2-D SSA.” After developing the suitable clutter removal algorithm, wepropose a complete algorithm chain for pipeline imaging. An UWB nearfieldSARmonitoring system including anUWBM-sequence sensor
and automatic positioner are implemented and the image of drilled
perforations in a concrete pipe mimicking oil well structure as a case
study is reconstructed to test the proposed algorithm. Compared to
the literature, a comprehensive near-field SAR imaging algorithm
including new clutter removal is proposed and its performance is
verified by obtaining high-quality images in experimental results
A Depth-Adaptive Filtering Method for Effective GPR Tree Roots Detection in Tropical Area
This study presents a technique for processing Stepfrequency continuous wave
(SFCW) ground penetrating radar (GPR) data to detect tree roots. SFCW GPR is
portable and enables precise control of energy levels, balancing depth and
resolution trade-offs. However, the high-frequency components of the
transmission band suffers from poor penetrating capability and generates noise
that interferes with root detection. The proposed time-frequency filtering
technique uses a short-time Fourier transform (STFT) to track changes in
frequency spectrum density over time. To obtain the filter window, a weighted
linear regression (WLR) method is used. By adopting a conversion method that is
a variant of the chirp Z-Transform (CZT), the timefrequency window filters out
frequency samples that are not of interest when doing the frequency-to-time
domain data conversion. The proposed depth-adaptive filter window can
selfadjust to different scenarios, making it independent of soil information
and effectively determines subsurface tree roots. The technique is successfully
validated using SFCW GPR data from actual sites in a tropical area with
different soil moisture levels, and the two-dimensional (2D) radar map of
subsurface root systems is highly improved compared to existing methods.Comment: 10 pages, 12 figures, Accepted by IEEE TI
Modern GPR Target Recognition Methods
Traditional GPR target recognition methods include pre-processing the data by
removal of noisy signatures, dewowing (high-pass filtering to remove
low-frequency noise), filtering, deconvolution, migration (correction of the
effect of survey geometry), and can rely on the simulation of GPR responses.
The techniques usually suffer from the loss of information, inability to adapt
from prior results, and inefficient performance in the presence of strong
clutter and noise. To address these challenges, several advanced processing
methods have been developed over the past decade to enhance GPR target
recognition. In this chapter, we provide an overview of these modern GPR
processing techniques. In particular, we focus on the following methods:
adaptive receive processing of range profiles depending on the target
environment; adoption of learning-based methods so that the radar utilizes the
results from prior measurements; application of methods that exploit the fact
that the target scene is sparse in some domain or dictionary; application of
advanced classification techniques; and convolutional coding which provides
succinct and representatives features of the targets. We describe each of these
techniques or their combinations through a representative application of
landmine detection.Comment: Book chapter, 56 pages, 17 figures, 12 tables. arXiv admin note:
substantial text overlap with arXiv:1806.0459
- …