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

The SIMCA algorithm for processing ground penetrating radar data and its practical applications

The main objective of this thesis is to present a new image processing technique to improve the detectability of buried objects such as landmines using Ground Penetrating Radar (GPR). The main challenge of GPR based landmine detection is to have an accurate image analysis method that is capable of reducing false alarms. However an accurate image relies on having sufficient spatial resolution in the received signal. An Antipersonnel mine (APM) can have a diameter as little as 2cm, whereas many soils have very high attenuation at frequencies above 450 MHz. In order to solve the detection problem, a system level analysis of the issues involved with the recognition of landmines using image reconstruction is required. The thesis illustrates the development of a novel technique called the SIMCA (“SIMulated Correlation Algorithm”) based on area or volume correlation between the trace that would be returned by an ideal point reflector in the soil conditions at the site (obtained using the realistic simulation of Maxwell’s equations) and the actual trace. During an initialization phase, SIMCA carries out radar simulation using the system parameters of the radar and the soil properties. Then SIMCA takes the raw data as the radar is scanned over the ground and uses a clutter removal technique to remove various unwanted signals of clutter such as cross talk, initial ground reflection and antenna ringing. The trace which would be returned by a target under these conditions is then used to form a correlation kernel using a GPR simulator. The 2D GPR scan (B scan), formed by abutting successive time-amplitude plots taken from different spatial positions as column vectors,is then correlated with the kernel using the Pearson correlation coefficient resulting in a correlated image which is brightest at points most similar to the canonical target. This image is then raised to an odd power >2 to enhance the target/background separation. The first part of the thesis presents a 2-dimensional technique using the B scans which have been produced as a result of correlating the clutter removed radargram (’B scan’) with the kernel produced from the simulation. In order to validate the SIMCA 2D algorithm, qualitative evidence was used where comparison was made between the B scans produced by the SIMCA algorithm with B scans from some other techniques which are the best alternative systems reported in the open literature. It was found from this that the SIMCA algorithm clearly produces clearer B scans in comparison to the other techniques. Next quantitative evidence was used to validate the SIMCA algorithm and demonstrate that it produced clear images. Two methods are used to obtain this quantitative evidence. In the first method an expert GPR user and 4 other general users are used to predict the location of landmines from the correlated B scans and validate the SIMCA 2D algorithm. Here human users are asked to indicate the location of targets from a printed sheet of paper which shows the correlated B scans produced by the SIMCA algorithm after some training, bearing in mind that it is a blind test. For the second quantitative evidence method, the AMIRA software is used to obtain values of the burial depth and position of the target in the x direction and hence validate the SIMCA 2D algorithm. Then the absolute error values for the burial depth along with the absolute error values for the position in the x direction obtained from the SIMCA algorithm and the Scheers et al’s algorithm when compared to the corresponding ground truth values were calculated. Two-dimensional techniques that use B scans do not give accurate information on the shape and dimensions of the buried target, in comparison to 3D techniques that use 3D data (’C scans’). As a result the next part of the thesis presents a 3-dimensional technique. The equivalent 3D kernel is formed by rotating the 2D kernel produced by the simulation along the polar co-ordinates, whilst the 3D data is the clutter removed C scan. Then volume correlation is performed between the intersecting parts of the kernel and the data. This data is used to create iso-surfaces of the slices raised to an odd power > 2. To validate the algorithm an objective validation process which compares the actual target volume to that produced by the re-construction process is used. The SIMCA 3D technique and the Scheers et al’s (the best alternative system reported in the open literature) technique are used to image a variety of landmines using GPR scans. The types of mines included plastic, wooden and glass ones. In all cases clear images were obtained with SIMCA. In contrast Scheers’ algorithm, the present state-of-the-art, failed to provide clear images of non metallic landmines. For this thesis, the above algorithms have been tested for landmine data and for locating foundations in demolished buildings and to validate and demonstrate that the SIMCA algorithms are better than existing technologies such as the Scheers et al’s method and the REFLEXW commercial software

Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

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

DETERMINE: Novel Radar Techniques for Humanitarian Demining

Today the plague of landmines represent one of the greatest curses of modern time, killing and maiming innocent people every day. It is not easy to provide a global estimate of the problem dimension, however, reported casualties describe that the majority of the victims are civilians, with almost a half represented by children. Among all the technologies that are currently employed for landmine clearance, Ground Penetrating Radar (GPR) is one of those expected to increase the efficiency of operation, even if its high-resolution imaging capability and the possibility of detecting also non-metallic landmines are unfortunately balanced by the high sensor false alarm rate. Most landmines may be considered as multiple layered dielectric cylinders that interact with each other to produce multiple reflections, which will be not the case for other common clutter objects. Considering that each scattering component has its own angular radiation pattern, the research has evaluated the improvements that multistatic configurations could bring to the collected information content. Employing representative landmine models, a number of experimental campaigns have confirmed that GPR is capable of detecting the internal reflections and that the presence of such scattering components could be highlighted changing the antennas offset. In particular, results show that the information that can be extracted relevantly changes with the antenna separation, demonstrating that this approach can provide better confidence in the discrimination and recognition process. The proposed bistatic approach aims at exploiting possible presence of internal structure beneath the target, which for landmines means the activation or detonation assemblies and possible internal material diversity, maintaining a limited acquisition effort. Such bistatic configurations are then included in a conceptual design of a highly flexible GPR system capable of searching for landmines across a large variety of terrains, at reasonably low cost and targeting operators safety

Inertial MEMS: readout, test and application

This thesis moves towards the investigation of Micro Electro-Mechanical Systems (MEMS) intertial sensors from different perspectives and points of view: readout, test and application. Chapter 1 deals with the state-of-the-art for the interfaces usually employed for 3- axes micromachined gyroscopes. Several architecture based on multiplexing schemes in order to extremely simplify the analog front-end which can be based on a single charge amplifier are analysed and compared. A novel solution that experiments an innovative readout technique based on a special analog-Code Division Multiplexing Access (CDMA) is presented; this architecture can reach a considerable reduction of the Analog Front-End (AFE) with reference to other multiplexing schemes. Many family codes have been considered in order to find the best trade-off between performance and complexity. System-level simulations prove the effectiveness of this technique in processing all the required signals. A case study is also analysed: a comparison with the SD740 micro-machined integrated inertial module with tri-axial gyroscope by SensorDynamics AG is provided. MEMS accelerometers are widely used in the automotive and aeronautics fields and are becoming extremely popular in a wide range of consumer electronics products. The cost of testing is a major one within the manufacturing process, because MEMS accelerometer characterization requires a series of tests that include physical stimuli. The calibration and the functional testing are the most challenging and a wide selection of Automatic Test Equipments (ATEs) is available on the market for this purpose; those equipments provide a full characterization of the Device Under Test (DUT), from low-g to high-g levels, even over temperature. Chapter 2 presents a novel solution that experiments an innovative procedure to perform a characterization at medium-g levels. The presented approach can be applied to low-cost ATEs obtaining challenging results. The procedure is deeply investigated and an experimental setup is described. A case study is also analysed: some already trimmed Three Degrees of Freedom (3DoF)-Inertial Measurement Unit (IMU) modules (three-axes accelerometer integrated with a mixed signal ASIC), from SensorDynamics AG are tested with the experimental setup and analysed, for the first time, at medium-g levels. Standard preprocessing techniques for removing the ground response from vehicle- mounted Ground Penetrating Radar (GPR) data may fail when used on rough terrain. In Chapter 3, a Laser Imaging Detection and Ranging (LIDAR) system and a Global Positioning System (GPS)/IMU is integrated into a prototype system with the GPR and provided high-resolution measurements of the ground surface. Two modifications to preprocessing were proposed for mitigating the ground bounce based on the available LIDAR data. An experiment is carried out on a set of GPR/LIDAR data collected with the integrated prototype vehicle over lanes with artificially rough terrain, consisting of targets buried under or near mounds, ruts and potholes. A stabilization technique for multi-element vehicle-mounted GPR is also presented

Deep learning processing and interpretation of ground penetrating radar data using a numerical equivalent of a real GPR transducer

Ground-Penetrating Radar (GPR) is a popular non-destructive electromagnetic (EM) technique that is used in diverse applications across different fields, most commonly geophysics and civil engineering. One of the most common applications of GPR is concrete scanning, where it is used to detect structural elements and support the assessment of its condition. However, in any GPR application, the data have no resemblance to the characteristics of targets of interest and a means of extracting information from the data regarding the targets is required. Interpreting the GPR data, to infer key properties of the subsurface and to locate the targets is a difficult and challenging task and is highly dependent on the processing of the data and the experience of the user. Traditional processing techniques have some drawbacks, which can lead to misinterpretations of the data in addition to the interpretation being subjective to the user. Machine learning (ML) has proven its ability to solve a variety of problems and map complex relationships and in recent years, is becoming an increasingly attractive option for solving GPR and other EM problems regarding processing and interpretation. Numerical modelling has been extensively used to understand the EM wave propagation and assist in the interpretation of GPR responses. If ML is combined with numerical modelling, efficient solutions to GPR problems can be acquired. This research focuses on developing a numerical equivalent of a commercial GPR transducer and utilising this model to produce realistic synthetic training data sets for deep learning applications. The numerical model is based on the high-frequency 2000 MHz "palm" antenna from Geophysical Survey Systems, Inc. (GSSI). This GPR system is mainly used for concrete scanning, where the targets are located close to the surface. Unknown antenna parameters were found using global optimisation by minimising the mismatch between synthetic and real responses. A very good match was achieved, demonstrating that the model can accurately replicate the behaviour of the real antenna which was further validated using a number of laboratory experiments. Real data were acquired using the GSSI transducer over a sandbox and reinforced concrete slabs and the same scenarios were replicated in the simulations using the antenna model, showing excellent agreement. The developed antenna model was used to generate synthetic data, which are similar to the true data, for two deep learning applications, trained entirely using synthetic data. The first deep learning application suggested in the present thesis is background response and properties prediction. Two coupled neural networks are trained to predict the background response given as input total GPR responses, perform background removal and subsequently use the predicted background response to predict its dielectric properties. The suggested scheme not only performs the background removal processing step, but also enables the velocity calculation of the EM wave propagating in a medium using the predicted permittivity value. The ML algorithm is evaluated using a number of synthetic and measured data demonstrating its efficiency and higher accuracy compared to traditional methods. Predicting a permittivity value per A-scan included in a B-scan results in a permittivity distribution, which is used along with background removal to perform reverse-time migration (RTM). The proposed RTM scheme proved to be superior when compared with the commonly used RTM schemes. The second application was a deep learning-based forward solver, which is used as part of a full-waveform inversion (FWI) framework. A neural network is trained to predict entire B-scans given certain model parameters as input for reinforced concrete slab scenarios. The network makes predictions in real time, reducing by orders of magnitude the computational time of FWI, which is usually coupled with an FDTD forward solver. Therefore, making FWI applicable to commercial computers without the need of high-performance computing (HPC). The results clearly illustrate that ML schemes can be implemented to solve GPR problems and highlight the importance of having a digital representation of a real transducer in the simulations

FMCW Signals for Radar Imaging and Channel Sounding

A linear / stepped frequency modulated continuous wave (FMCW) signal has for a long time been used in radar and channel sounding. A novel FMCW waveform known as “Gated FMCW” signal is proposed in this thesis for the suppression of strong undesired signals in microwave radar applications, such as: through-the-wall, ground penetrating, and medical imaging radar. In these applications the crosstalk signal between antennas and the reflections form the early interface (wall, ground surface, or skin respectively) are much stronger in magnitude compared to the backscattered signal from the target. Consequently, if not suppressed they overshadow the target’s return making detection a difficult task. Moreover, these strong unwanted reflections limit the radar’s dynamic range and might saturate or block the receiver causing the reflection from actual targets (especially targets with low radar cross section) to appear as noise. The effectiveness of the proposed waveform as a suppression technique was investigated in various radar scenarios, through numerical simulations and experiments. Comparisons of the radar images obtained for the radar system operating with the standard linear FMCW signal and with the proposed Gated FMCW waveform are also made. In addition to the radar work the application of FMCW signals to radio propagation measurements and channel characterisation in the 60 GHz and 2-6 GHz frequency bands in indoor and outdoor environments is described. The data are used to predict the bit error rate performance of the in-house built measurement based channel simulator and the results are compared with the theoretical multipath channel simulator available in Matlab