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)

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

Noninvasive suspicious liquid detection using wireless signals

Conventional liquid detection instruments are very expensive and not conducive to large-scale deployment. In this work, we propose a method for detecting and identifying suspicious liquids based on the dielectric constant by utilizing the radio signals at a 5G frequency band. There are three major experiments: first, we use wireless channel information (WCI) to distinguish between suspicious and nonsuspicious liquids; then we identify the type of suspicious liquids; and finally, we distinguish the different concentrations of alcohol. The K-Nearest Neighbor (KNN) algorithm is used to classify the amplitude information extracted from the WCI matrix to detect and identify liquids, which is suitable for multimodal problems and easy to implement without training. The experimental result analysis showed that our method could detect more than 98% of the suspicious liquids, identify more than 97% of the suspicious liquid types, and distinguish up to 94% of the different concentrations of alcohol

Radar Systems for Glaciology

This chapter deals with radar systems, measurements and instrumentation employed to study the internal core and bedrock of ice sheets in glaciology. The Earth's ice sheets are in Greenland and Antarctica. They cover about 10% of the land surface of the planet. The total accumulated ice comprises 90% of the global fresh water reserve. These ice sheets, associated with the ocean environment, provide a major heat sink which significantly modulates climate. Glaciology studies aim to understand the various process involved in the flow (dynamics), thermodynamics, and long-term behaviour of ice sheets. Studies of large ice masses are conducted in adverse environmental conditions (extreme cold, long periods of darkness). The development of remote sensing techniques have played an important role in obtaining useful results. The most widely used techniques are radar systems, employed since the 1950s in response to a need to provide a rapid and accurate method of measuring ice thickness. Year by year, polar research has become increasingly important because of global warming. Moreover, the discovery of numerous subglacial lake areas (water entrapped beneath the ice sheets) has attracted scientific interest in the possible existence of water circulation between lakes or beneath the ice (Kapitsa et al., 2006; Wingham et al., 2006; Bell et al., 2007). Recent studies in radar signal shape and amplitude could provide evidence of water circulation below the ice (Carter 2007, Oswald and Gogineni 2008). In this chapter the radar systems employed in glaciology, radio echo sounding (RES), are briefly described with some interesting results. RES are active remote sensing systems that utilize electromagnetic waves that penetrate the ice. They are used to obtain information about the electromagnetic properties of different interfaces (for example rock-ice, ice-water, seawater-ice) that reflect the incoming signal back to the radar. RES systems are characterized by a high energy (peak power from 10 W to 10 KW) variable transmitted pulse width (about from 0.5 ns to several microseconds) in order to investigate bedrock characteristics even in the thickest zones of the ice sheets (4755 m is the deepest ice thickness measured in Antarctica using a RES system). Changing the pulse length or the transmitted signal frequencies it is possible to investigate particular ice sheet details with different resolution. Long pulses allows transmission of higher power than short pulses, penetrating the thickest parts of the ice sheets but, as a consequence, resolution decreases. For example, the GPR system, commonly used in geophysics for rock, soil, ice, fresh water, pavement and structure characterization, employs a very short transmitted pulse (0.5 ns to 10 ns) that allow detailing of the shallow parts of an ice sheet (100-200 m in depth) (Reynolds 1997). Consequently, in recent years, GPR systems are also employed by explorers to find hidden crevasses on glaciers for safety. RES surveys have been widely employed in Antarctic ice sheet exploration and they are still an indispensable tool for mapping bedrock morphologies and properties of the last unexplored continent on Earth. The advantage of using these remote sensing techniques is that they allow large areas to be covered, in good detail and in short times using platforms like aeroplanes and surface vehicles

OFDM Coupled Compressive Sensing Algorithm for Stepped Frequency Ground Penetrating Radar

Dating back to as far as 1940, the US road and bridge infrastructure system has garnered quite the status for strategically connecting together half a continent. As monumental as the infrastructure\u27s status, is its rate of deterioration, with the average bridge age coming at a disconcerting 50 years. Aside from visual inspection, a battery of non-destructive tests were developed to conduct structural fault assessment and detect laminations, in order to preemptively take preventive measures. The mainstream commercially favored test is the impulse time domain ground penetrating radar (GPR). An extremely short, high voltage pulse is used to visualize cross-sections of the bridge decks. While effective and it does not disturb traffic flow, impulse radar suffers from major drawbacks. The drawbacks are namely, its limited dynamic range and high cost of system manufacturing. A less prominent yet highly effective system, stepped frequency continuous wave (SFCW) GPR, was developed to address the aforementioned drawbacks. Mostly developed for research centers and academia, SFCW boasts a high dynamic range and low cost of system manufacturing, while producing comparable if not identical results to the impulse counterpart. However, data procurement speed is an inherent problem in SFCW GPR, which seems to keep impulse radar in the lead for production and development. I am proposing a novel approach to elevate SFCW\u27s data acquisition speed and its scanning efficiency altogether. This approach combines an encoding method called orthogonal frequency division multiplexing (OFDM) and an emerging paradigm called compressive sensing (CS). In OFDM, a digital data stream, the transmit signal, is encoded on multiple carrier frequencies. These frequencies are combined in such a way to achieve orthogonality between the carrier frequencies, while mitigating any interference between said frequencies. In CS, a signal can be potentially reconstructed from a few samples below the standardized Nyquist rate. A novel design of the SFCW GPR architecture coupled with the OFDM-CS algorithm is proposed and evaluated using ideal channels and realistically modelled bridge decks

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE