Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Synthetic aperture radar imaging system for landmine detection using a ground penetrating radar on board a unmanned aerial vehicle

This paper presents a novel system to obtain images from the underground based on ground penetrating radar (GPR). The proposed system is composed by a radar module mounted on board an unmanned aerial vehicle (UAV), which allows the safe inspection of difficult-to-access areas without being in direct contact with the soil. Therefore, it can be used to detect dangerous buried objects, such as landmines. The radar measurements are coherently combined using a synthetic aperture radar (SAR) algorithm, which requires cm-level accuracy positioning system. In addition, a clutter removal technique is applied to mitigate the reflection at the air-soil interface (which is caused by impedance mismatching). Besides the aforementioned advantages, the system can detect both metallic and dielectric targets (due to the use of a radar instead of a metal detector) and it allows to obtain high-resolution underground images (due to the SAR processing). The algorithms and the UAV payload are validated with measurements in both controlled and real scenarios, showing the feasibility of the proposed system.Ministerio de Economía y Competitividad | Ref. TEC2014-54005-PMinisterio de Economía y Competitividad | Ref. TEC2014-55290-JINMinisterio de Economía y Competitividad | Ref. TEC2015-73908-JINMinisterio de Economía y Competitividad | Ref. TEC2015-65353-RAgencia Estatal de Investigación | Ref. RYC-2016-20280Ministerio de Educación | Ref. FPU15/06341Gobierno del Principado de Asturias | Ref. PCTI 2013-2017Gobierno del Principado de Asturias | Ref. FC-15-GRUPIN14-114Gobierno del Principado de Asturias | Ref. IDI/2017/000095Xunta de Galicia | Ref. GRC2015/01

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

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

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

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

Synthetic Aperture Radar Imaging System for Landmine Detection Using a Ground Penetrating Radar on Board a Unmanned Aerial Vehicle

This paper presents a novel system to obtain images from the underground based on ground penetrating radar (GPR). The proposed system is composed by a radar module mounted on board an unmanned aerial vehicle (UAV), which allows the safe inspection of dif cult-to-access areas without being in direct contact with the soil. Therefore, it can be used to detect dangerous buried objects, such as landmines. The radar measurements are coherently combined using a synthetic aperture radar (SAR) algorithm, which requires cm-level accuracy positioning system. In addition, a clutter removal technique is applied to mitigate the re ection at the air-soil interface (which is caused by impedance mismatching). Besides the aforementioned advantages, the system can detect both metallic and dielectric targets (due to the use of a radar instead of a metal detector) and it allows to obtain high-resolution underground images (due to the SAR processing). The algorithms and the UAV payload are validated with measurements in both controlled and real scenarios, showing the feasibility of the proposed system