Signal Processing Techniques for Landmine Detection Using Impulse Ground Penetrating Radar (ImGPR)

Landmines and unexploded ordinance (UXO) are laid during a conflict against enemy forces. However, they kill or maim civilians decades after the conflict has ended. There are more than 110 million landmines actively lodged in the globe. Every year more than 26,000 innocent civilians are killed or maimed. Most modern landmines are mainly nonmetallic or plastic, which are difficult to be detected using conventional metal detectors. Detection using hand-held prodding is a slow and expensive process. Impulse Ground Penetrating Radar (ImGPR) is a nondestructive technique capable of detecting shallowly buried nonmetallic anti-personnel (AP) and anti-tank (AT) landmines. In this PhD thesis, ImGPR is considered as a tool to detect landmines and UXO. The presence of strong ground clutter and noise degrade the performance of GPR. Hence, using a GPR sensor is almost impossible without the application of sophisticated signal processing. In electromagnetic wave propagation modeling, a multilayer transmission line technique is applied. It considers different soil types at different moisture levels. Plastic targets of different diameters are buried at different depths. The modeled signal is then used to estimate the ground and buried target parameters. In a parameter estimation procedure, a surface reflection parameter method (SRPM) is applied. Signal processing algorithms are implemented for clutter reduction and decision making purposes. Attention is mainly given to the development of techniques, that are applicable to real-time landmine detection. Advanced techniques are preceded by elementary preprocessing techniques, which are useful for signal correction and noise reduction. Background subtraction techniques based on multilayer modeling, spatial filtering and adaptive background subtraction are implemented. In addition to that, decorrelation and symmetry filtering techniques are also investigated. In the correlated decision fusion framework, local decisions are transmitted to the fusion center so as to compute a global decision. In this case, the concept of confidence information of local decisions is crucial to obtain acceptable detection results. The Bahadur-Lazarsfeld and Chow expansions are used to estimate the joint probability density function of the correlated decisions. Furthermore, a decision fusion based on fuzzy set is implemented. All proposed methods are evaluated using simulated as well as real GPR data measurements of many scenarios. The real data collection campaign took place at the Griesheim old airport and Botanischer Garten, Darmstadt, Germany in July 2011

A generic framework for context-dependent fusion with application to landmine detection.

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 a viable alternative to using a single classifier. Over the past few years, a variety of schemes have been proposed for combining multiple classifiers. Most of these were global as they assign a degree of worthiness to each classifier, that is averaged over the entire training data. This may not be the optimal way to combine the different experts since the behavior of each one may not be uniform over the different regions of the feature space. To overcome this issue, few local methods have been proposed in the last few years. Local fusion methods aim to adapt the classifiers\u27 worthiness to different regions of the feature space. First, they partition the input samples. Then, they identify the best classifier for each partition and designate it as the expert for that partition. Unfortunately, current local methods are either computationally expensive and/or perform these two tasks independently of each other. However, feature space partition and algorithm selection are not independent and their optimization should be simultaneous. In this dissertation, we introduce a new local fusion approach, called Context Extraction for Local Fusion (CELF). CELF was designed to adapt the fusion to different regions of the feature space. It takes advantage of the strength of the different experts and overcome their limitations. First, we describe the baseline CELF algorithm. We formulate a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. The context identification component thrives to partition the input feature space into different clusters (called contexts), while the fusion component thrives to learn the optimal fusion parameters within each cluster. Second, we propose several variations of CELF to deal with different applications scenario. In particular, we propose an extension that includes a feature discrimination component (CELF-FD). This version is advantageous when dealing with high dimensional feature spaces and/or when the number of features extracted by the individual algorithms varies significantly. CELF-CA is another extension of CELF that adds a regularization term to the objective function to introduce competition among the clusters and to find the optimal number of clusters in an unsupervised way. CELF-CA starts by partitioning the data into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose the competition gradually become depleted and vanish. Third, we propose CELF-M that generalizes CELF to support multiple classes data sets. The baseline CELF and its extensions were formulated to use linear aggregation to combine the output of the different algorithms within each context. For some applications, this can be too restrictive and non-linear fusion may be needed. To address this potential drawback, we propose two other variations of CELF that use non-linear aggregation. The first one is based on Neural Networks (CELF-NN) and the second one is based on Fuzzy Integrals (CELF-FI). The latter one has the desirable property of assigning weights to subsets of classifiers to take into account the interaction between them. To test a new signature using CELF (or its variants), each algorithm would extract its set of features and assigns a confidence value. Then, the features are used to identify the best context, and the fusion parameters of this context are used to fuse the individual confidence values. For each variation of CELF, we formulate an objective function, derive the necessary conditions to optimize it, and construct an iterative algorithm. Then we use examples to illustrate the behavior of the algorithm, compare it to global fusion, and highlight its advantages. We apply our proposed fusion methods to the problem of landmine detection. We use data collected using Ground Penetration Radar (GPR) and Wideband Electro -Magnetic Induction (WEMI) sensors. We show that CELF (and its variants) can identify meaningful and coherent contexts (e.g. mines of same type, mines buried at the same site, etc.) and that different expert algorithms can be identified for the different contexts. In addition to the land mine detection application, we apply our approaches to semantic video indexing, image database categorization, and phoneme recognition. In all applications, we compare the performance of CELF with standard fusion methods, and show that our approach outperforms all these methods

Detection of explosives

The extensive literature on the methods of detection of explosives under the stationary and field conditions is reviewed. Today, there are hundreds of publications on detectability of explosives by different methods, but none of the methods gives a 100% guarantee of explosives detection. Consideration has been given to advanced high-technology methods of explosives detection: nuclear quadrupole resonance method, nuclear physics methods (slow and fast neutrons, gamma-quanta), biological methods. Notice that the danger of newly created explosives, including land mines, increases. The actuation systems are also constantly improved. Most difficulties with detection, extraction and deactivation of explosive devices can be overcome with the help of updated methods and techniques. The present review calls attention primarily to the possibility of solving the pressing and complicated problems using charged particle accelerators.Проведен обзор литературы по методам детектирования взрывчатых веществ (ВВ) в стационарных и полевых условиях. В настоящее время имеются сотни публикаций по возможности детектирования ВВ различными методами, но 100% гарантии их обнаружения нет. Внимание уделено современным высокотехнологическим методам обнаружения ВВ: ядерному квадрупольному резонансу, ядерно-физическим (медленные и быстрые нейтроны, гамма-кванты) и биологическим. Следует отметить, что опасность вновь создаваемых ВВ, в том числе и полевых мин, увеличивается. Совершенствуются и системы их срабатывания. Большинство трудностей по детектированию, извлечению и деактивации взрывных устройств может быть преодолено с помощью современных методов и технологий. Данный обзор, прежде всего, обращает внимание на возможность решения этой острой и сложной проблемы с помощью ускорителей заряженных частиц.Проведено огляд літератури по методах детектування вибухових речовин (ВР) у стаціонарних і польових умовах. На даний час є сотні публікацій по можливості детектування ВР різними методами, але 100% гарантії їх виявлення немає. Увага приділена сучасним високотехнологічним методам виявлення ВР: ядерному квадрупольному резонансу, ядерно-фізичним (повільні й швидкі нейтрони, гамма-кванти) і біологічним. Слід зазначити, що небезпека створюваних ВР, у тому числі польових мін, збільшується. Удосконалюються й системи їхнього спрацьовування. Більшість труднощів по детектуванню, добуванню і деактивації вибухових пристроїв може бути подолане за допомогою сучасних методів і технологій. Даний огляд, насамперед, звертає увагу на можливість рішення цієї гострої і складної проблеми за допомогою прискорювачів заряджених частинок

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

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

A Multidisciplinary Analysis of Frequency Domain Metal Detectors for Humanitarian Demining

This thesis details an analysis of metal detectors (low frequency electromagnetic induction devices) with emphasis on Frequency Domain (FD) systems and the operational conditions of interest to humanitarian demining. After an initial look at humanitarian demining and a review of their basic principles we turn our attention to electromagnetic induction modelling and to analytical solutions to some basic FD direct (forward) problems. The second half of the thesis focuses then on the analysis of an extensive amount of experimental data. The possibility of target classification is first discussed on a qualitative basis, then quantitatively. Finally, we discuss shape and size determination via near field imaging

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

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