Numerical methods for computing the modal decomposition of the magnetic polarizability of conducting objects

Numerical methods are presented for characterizing the wideband responses of conducting objects to excitation by electromagnetic induction sensors. Electromagnetic induction sensors can be used to measure the magnetic polarizability tensor of conducting targets, a tensor that encapsulates the entire scattering interaction between target and sensor. Wideband characterization of the magnetic polarizability tensor can be achieved by expanding the frequency response in pole-expansion form. The pole-expansion coefficients may then be used as a signature, which can be used for subsurface target detection. To derive the coefficients numerically, the interaction between a target of interest and the sensor is modeled as a linear system, which can then be set up as generalized eigenvalue problem. The eigenvalues of the system correspond to the pole locations of the pole expansion. The remaining coefficients can be derived from the eigenvectors of the system, which correspond to the that natural modes that are excited by the sensor. Integral methods are presented for numerically computing the pole-expansion coefficients of thin conducting sheets and shells, conducting volumes, and bodies of revolution. A differential method is also presented for conducting volumes. Computational results are compared to known analytical solutions when possible as well as to experimental measurements.Ph.D

Statistical and adaptive signal processing for UXO discrimination for next-generation sensor data

Abstract-Until recently, detection algorithms could not reliably distinguish between buried UXO and clutter, leading to many false alarms. Over the last several years modern geophysical techniques have been developed that merge more sophisticated sensors, underlying physical models, statistical signal processing algorithms, and adaptive training techniques. These new approaches have dramatically reduced false alarm rates, although for the most part they have been applied to data collected at sites with relatively benign topology and anomaly densities. On more challenging sites, performance of even these more modern discrimination approaches is still quite poor. As a result, efforts are underway to develop a new generation of UXO sensors that will produce data streams of multi-axis vector or gradiometric measurements, for which optimal processing has not yet been carefully considered or developed. We describe a research program to address this processing gap, employing a synergistic use of advanced phenomenologicalmodeling and signal-processing algorithms. The key foci of the program are (1) development of new physics-based signal processing approaches applicable to the problem in which vector data is available from such sensors; and (2) development of the theory of optimal experiments to guide the optimal design and deployment of the new sensor modalities. Here, we present initial results using simulated data obtained with our phenomenological models that indicate that optimal processing of features extracted from multi-axis EMI data can provide substantial improvements in discrimination performance over processing of features extracted from single-axis data

Classification of Metallic Targets Using a Walk-Through Metal Detection Portal

Metal detectors have been used for a long time for treasure hunting, security screening, and finding buried objects such as landmines or unexploded ordnance. Walk-through metal detection (WTMD) portals are used for making sure that forbidden or threatening metallic items, such as knives or guns, are not carried into secure areas at critical locations such as airports, court rooms, embassies, and prisons.The 9/11 terrorist act has given rise to stricter rules for aviation security worldwide, and the ensuing tighter security procedures have meant that passengers face more delays at airports. Moreover, the fear of terrorism has led to the adoption of security screening technology in a variety of places such as railway and coach stations, sports events, malls, and nightclubs.However, the current WTMD technology and scanning procedures at airports require that all metallic items be removed from clothing prior to scanning, causing inconvenience. Furthermore, alarms are triggered by innocuous items such as shoe shanks and artificial joints, along with overlooked items such as jewellery and belts. These lead to time- consuming, manual pat-down searches, which are found inconvenient, uncomfortable, and obtrusive by some.Modern WTMD portals are very sensitive devices that can detect items with only small amounts of metal, but they currently lack the ability to further classify the detected item. However, if a WTMD portal were able to classify objects reliably into, e.g., “knives”, “belts”, “keys”, the need for removing the items prior to screening would disappear, enabling a paradigm shift in the field of security screening.This thesis is based on novel research presented in five peer-reviewed publications. The scope of the problem has been narrowed down to a situation in which only one metallic item is carried through the portal at a time. However, the methods and results presented in this thesis can be generalized into a multi-object scenario. It has been shown that by using a WTMD portal and the magnetic polarisability tensor, it is possible to accurately distinguish between threatening and innocuous targets and to classify them into 10 to 13 arbitrary classes. Furthermore, a data library consisting of natural walk-throughs has been collected, and it has been demonstrated that the walk-through data collected with the above portal are subject to phenomena that might affect classification, in particular a bias and the so-called body effect. However, the publications show that, by using realistic walk-through data, high classification accuracy can be maintained regardless of the above problems. Furthermore, a self-diagnostics method for detecting unreliable samples has also been presented with potential to significantly increase classification accuracy and the reliability of decision making.The contributions presented in this thesis have a variety of implications in the field of WTMD-based security screening. The novel technology offers more information, such as an indication of the probable cause of the alarm, to support the conventional screening procedure. Moreover, eliminating the need for removing all metallic items prior to screening enables design of new products for scenarios such as sports events, where conventional screening procedures might be inconvenient, creating thus new business possibilities for WTMD manufacturing companies.The positive results give rise to a variety of future research topics such as using wideband data, enabling simultaneous classification of multiple objects, and developing the portal coil design to diminish signal nonlinearities. Furthermore, the ideas and the basic principles presented in this thesis may be applied to other metal detection applications, such as humanitarian demining

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

Numerical methods for electromagnetic wave propagation and scattering in complex media

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Vita.Includes bibliographical references (p. 227-242).Numerical methods are developed to study various applications in electromagnetic wave propagation and scattering. Analytical methods are used where possible to enhance the efficiency, accuracy, and applicability of the numerical methods. Electromagnetic induction (EMI) sensing is a popular technique to detect and discriminate buried unexploded ordnance (UXO). Time domain EMI sensing uses a transient primary magnetic field to induce currents within the UXO. These currents induce a secondary field that is measured and used to determine characteristics of the UXO. It is shown that the EMI response is difficult to calculate in early time when the skin depth is small. A new numerical method is developed to obtain an accurate and fast solution of the early time EMI response. The method is combined with the finite element method to provide the entire time domain response. The results are compared with analytical solutions and experimental data, and excellent agreement is obtained. A fast Method of Moments is presented to calculate electromagnetic wave scattering from layered one dimensional rough surfaces. To facilitate the solution, the Forward Backward method with Spectral Acceleration is applied. As an example, a dielectric layer on a perfect electric conductor surface is studied. First, the numerical results are compared with the analytical solution for layered flat surfaces to partly validate the formulation. Second, the accuracy, efficiency, and convergence of the method are studied for various rough surfaces and layer permittivities. The Finite Difference Time Domain (FDTD) method is used to study metamaterials exhibiting both negative permittivity and permeability in certain frequency bands.(cont.) The structure under study is the well-known periodic arrangement of rods and split-ring resonators, previously used in experimental setups. For the first time, the numerical results of this work show that fields propagating inside the metamaterial with a forward power direction exhibit a backward phase velocity and negative index of refraction. A new metamaterial design is presented that is less lossy than previous designs. The effects of numerical dispersion in the FDTD method are investigated for layered, anisotropic media. The numerical dispersion relation is derived for diagonally anisotropic media. The analysis is applied to minimize the numerical dispersion error of Huygens' plane wave sources in layered, uniaxial media. For usual discretization sizes, a typical reduction of the scattered field error on the order of 30 dB is demonstrated. The new FDTD method is then used to study the Angular Correlation Function (ACF) of the scattered fields from continuous random media with and without a target object present. The ACF is shown to be as much as 10 dB greater when a target object is present for situations where the target is undetectable by examination of the radar cross section only.by Christopher D. Moss.Ph.D

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

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium