Wideband Electromagnetic Induction Research Articles

Wideband Models for the Electromagnetic Induction Signatures of Thin Conducting Shells

Wideband electromagnetic induction sensors have been shown to be more effective than traditional metal detectors at identifying metallic targets of interest when buried near abundant metallic clutter. A promising strategy for performing target identification using wideband data relies on characterizing the target response using a magnetic polarizability tensor (MPT) that is a function of frequency. In this paper, a surface integral method is presented for deriving the MPT in singularity expansion form using a modal decomposition. The method is verified by comparing the singularity expansion for a spherical shell to a derived analytical solution. The expansion coefficients for cylindrical tubes, of various aspect ratios, are compared to experimental results, showing good agreement. Finally, expansion coefficients are given for a disk and are compared to other numerical results.

Low-Rank Model for Wideband Electromagnetic Induction Sensors

Wideband electromagnetic induction (WEMI) sensors are used to detect, classify, and locate obscured targets. WEMI sensors can be designed to measure the magnetic fields either in the time domain or frequency domain. They also capture multiple location measurements from a target by either a physical sensor array, a synthetic sensor array created by scanning the sensor, or a combination of the two. This letter uses a physical model for the sensor to develop a low-rank model for WEMI data. The data are organized into a matrix where the rows contain the time/frequency measurements and the columns contain the multiple locations so all of the data can be exploited jointly. It is shown that this data matrix can be processed directly with singular value decomposition (SVD) to extract three independent terms—one for target signature, one related to location, and one for tensors that define the orientation. The low-rank model predicts a maximum rank for WEMI measurements and is exploited to provide a relationship between the rank of the measurements and the number of linearly independent tensors for the target. Results are presented from the laboratory data to validate the low-rank model and demonstrate the connection between the data’s rank and the physical target. The low-rank model leads to a new “filterless” processing paradigm for exploiting WEMI data by using the SVD to perform sensor calibration, hardware debugging, as well as target detection.

EBG Antenna for GPR Colocated With a Metal Detector for Landmine Detection

Electromagnetic band-gap (EBG) antennas are ideal for handheld sensing applications, such as ground-penetrating radar (GPR) for landmine detection, because of their small size, efficiency, and directivity. Increased detection performance has been shown when a GPR is combined with a metal detector, but a typical EBG antenna would preclude the sensors from being colocated because of the large amount of metal in the EBG structure. An EBG composed of very thin metal is proposed in this letter for application as a GPR colocated with a metal detector, without causing a significant self-response in the metal detector. Manufacturing methods are discussed, and GPR measurements are shown from the thin-metal EBG antennas. The metal detector response from a thin metal sheet is discussed, and measurements of the EBG ground planes are shown using a laboratory wideband electromagnetic induction system. The response from the thin metal is shown to be as low as three orders of magnitude less than from a copper sheet at typical metal detector frequencies.

Simple wideband models for disks and wires in the eddy current approximation

Abstract Wideband electromagnetic induction systems have shown improved false alarm rates when compared with traditional metal detectors. Calibration of these sensors and the development of algorithms for target discrimination could be assisted by a set of models for common targets. In this paper, simple wideband models of the eddy current response for a wire and disk are provided. These are provided in the form of a singularity expansion of the polarizability dyadic. In an effort to make this form more concrete, a major focus of the paper is on relating the terms of the expansion to graphs of the currents present on the disk. The models provided in the paper are based on limiting forms of a cylinder as computed using the body-of-revolutions finite element method. Measured polarizability dyadics are also shown to fit the forms provided reasonably well.

Adaptive Local Fusion With Fuzzy Integrals

We propose a novel method for fusing different classifiers outputs. Our approach, called context extraction for local fusion with fuzzy integrals (CELF-FI), is a local approach that adapts a fuzzy integrals fusion method to different regions of the feature space. It is based on a novel objective function that combines context identification and multialgorithm fusion criteria into a joint objective function. This objective function consists of two terms: The first is designed to produce compact clusters, called contexts, and the second is designed to produce Sugeno measures for fuzzy integral fusion for each context. The terms are optimized simultaneously via alternating optimization. To test a new sample, first, its features (extracted by each algorithm) are used to assign it to each context with a fuzzy membership degree. Second, the sample confidence values (assigned by each algorithm) are fused within each context using the learned context fusion parameters. Then, the context-dependent partial confidence values are weighted by the membership degrees and averaged over all contexts to produce a final confidence value. We illustrate the performance of CELF using synthetic data, and we apply it to the problem of landmine detection using ground penetrating radar and wideband electromagnetic induction. Our extensive experiments have indicated that the proposed fusion approach outperforms all individual classifiers, the global fuzzy integral fusion method, and the basic local fusion with linear aggregation.

GRANMA: Gradient Angle Model Algorithm on Wideband EMI Data for Land-Mine Detection

This letter presents a simple and fast algorithm to analyze wideband electromagnetic induction data for subsurface targets. A well-known four-parameter model is differentiated, resulting in a two-parameter model. A fast lookup table is used to find parameters as opposed to nonlinear optimization. The proposed approach provides a computationally faster way to reproduce the results of state-of-the-art methods. A detailed mathematical analysis of the model is given that describes the advantages and limitations of the proposed method.

Unexploded ordnance discrimination using magnetic and electromagnetic sensors: Case study from a former military site

In a study at a military range with the objective to discriminate potentially hazardous [Formula: see text] mortars from nonhazardous shrapnel, range, and cultural debris, six different discrimination techniques were tested using data from an array of magnetometers, a time-domain electromagnetic induction (EMI) cart, an array of time-domain sensors, and a time-domain EMI cart with a wider measurement bandwidth. Discrimination was achieved using rule-based or statistical classification of feature vectors extracted from dipole or polarization tensor models fit to detected anomalies. For magnetics, the ranking by moment yielded better discrimination results than that of apparent remanence from relatively large remanent magnetizations of several of the seeded items. The magnetometer results produced very accurate depths and fewer failed fits attributable to noisy data or model insuffi-ciency. The EMI-based methods were more effective than the magnetometer for intrinsic discrimination ability. The higher signal-to-noise ratio, denser coverage, and more precise positioning of the EM-array data resulted in fewer false positives than the EMI cart. When depth constraints from the magnetometer data were used to constrain the EMI fits through cooperative inversion, discrimination performance improved considerably. The wide-band EMI sensor was deployed in a cued-interrogation mode over a subset of anomalies. This produced the highest-quality data because of collecting the densest data around each target and the additional late time-decay information available with the wide-band sensor. When the depth from the magnetometer was used as a constraint in the cooperative inversion process, all [Formula: see text] mortars were recovered before any false positives were encountered.

Performance Comparison of Automated Induction-Based Algorithms for Landmine Detection in a Blind Field Test

Wideband Electromagnetic Induction (EMI) data provides an opportunity to apply robust statistical signal processing techniques to potentially mitigate false alarm rates in real-time landmine detection. This paper explores the application of matched subspace detectors (MSDs) and Support Vector Machines (SVMs) to this problem. A library of landmine responses is generated from a set of calibration data and a bank of matched subspace detectors, each designed to detect a specific mine type, is developed. Support vector machines are also considered for target/clutter discrimination. These are developed based on landmine signatures, decay rate estimates, and the outputs of matched subspace filter banks. Synthetic data sets are generated and matched subspace detectors and support vector machines are trained using this synthetic data. Receiver Operating Characteristics (ROCs) for matched subspace detectors and support vector machines are presented for both experimental and simulated data sets. The results indicate that substantial reductions in false alarm rates can be achieved using these techniques, but that simulated data sets may not provide accurate predictors of performance.

Application of the method of auxiliary sources to the wide-band electromagnetic induction problem

The Method of Auxiliary Sources (MAS) is formulated and applied to solution of wide-band electromagnetic induction problems involving highly conducting and possibly permeable metallic objects. Improved remote sensing discrimination of buried unexploded ordnance (UXO) motivates the study. The method uses elementary auxiliary magnetic charges and magnetic current elements to produce the unknown field. Auxiliary sources are located on virtual surfaces that usually conform to but do not coincide with the real surface of the object. Once the source coefficients are determined, the-secondary field can easily be found. The method involves no confrontations with source or Green's function singularities. It is capable of treating penetrable as well as nonpenetrable objects. Because the solution is composed of fields that automatically satisfy the governing equations, by construction, all approximation resides only in the enforcement of boundary conditions at matching (collocation) points. Accuracy in satisfying the boundary conditions can be evaluated explicitly using noncollocation points over the surface. This in turn allows one to identify problem areas on the surface and make intelligent adjustments of the source distributions, to improve solutions at minimal cost. A general 3D formulation is presented, and a version specialized to treat bodies of revolution is applied in the specific test cases discussed.

On the wideband EMI response of a rotationally symmetric permeable and conducting target

A simple and accurate model is presented for computation of the electromagnetic induction (EMI) resonant frequencies of canonical conducting and ferrous targets, in particular, finite-length cylinders and rings. The imaginary resonant frequencies correspond to the well known exponential decay constants of interest for time-domain EMI interaction with conducting and ferrous targets. The results of the simple model are compared to data computed numerically, via method-of-moments (MoM) and finite-element models. Moreover, the simple model is used to fit measured wideband EMI data from ferrous cylindrical targets (in terms of a small number of parameters). It is also demonstrated that the general model for the magnetic-dipole magnetization, in terms of a frequency-dependent diagonal dyadic, is applicable to general rotationally symmetric targets (not just cylinders and rings).

Simple phenomenological models for wideband frequency-domain electromagnetic induction

The authors propose three phenomenological models for wideband electromagnetic induction (EMI) response of buried conductors, such as unexploded ordnance (UXO) or metal parts in landmines. The models are based on analytic solutions for spheres, cylinders, and wire loops, and produce physically reasonable predictions for a variety of targets at all frequencies of interest including matches to theory in the low- and high-frequency limits. All three produce excellent fits to test data and run quickly enough to be of practical use in data inversion schemes. The authors present a three-parameter model capable of exactly matching permeable spheres and cylinders, a four-parameter version which adds the capability to match wire loops, and a five-parameter version which adds the capability to match signals due to driving bands, a feature found only on UXO. Driving bands, also called rotating bands, are soft metal rings near the tail of a projectile designed to engage rifles in the gun bore when the projectile Is fired. The author observe that driving bands produce a distinctive loop-like signal in EMI spectra, possibly because they are in the shape of a loop and typically have much greater conductivity than the body of the UXO. They demonstrate that the five-parameter model is capable of accurately fitting this signal and expressing its presence or absence through model fit parameters.

Classification of landmine-like metal targets using wideband electromagnetic induction

In their previous work, the authors have shown that the detectability of landmines can be improved dramatically by the careful application of signal detection theory to time-domain electromagnetic induction (EMI) data using a purely statistical approach. In this paper, classification of various metallic land-mine-like targets via signal detection theory is investigated using a prototype wideband frequency-domain EMI sensor. An algorithm that incorporates both a theoretical model of the response of such a sensor and the uncertainties regarding the target/sensor orientation is developed. This allows the algorithms to be trained without an extensive data collection. The performance of this approach is evaluated using both simulated and experimental data. The results show that this approach affords substantial classification performance gains over a standard approach, which utilizes the signature obtained when the sensor is centered over the target and located at the mean expected target/sensor distance, and thus ignores the uncertainties inherent in the problem. On the average, a 60% improvement is obtained.

Remote Sensing of an Underground Coal-Burn Cavity with a Wide-Band Induction System

A conducting cavity in an underground coal burn was remotely probed from the surface with a wide-band electromagnetic induction system. The cavity, located near Hanna, WY, was produced by underground gasification of a 30-ft-thick subbituminous-coal seam ranging in depth from 300 to 400 ft. The cavity filled with saline ground water after the burn. The wide-band loop-loop system employed pseudonoise and cross-correlation techniques to produce a transient-time response in the field. Additional computer processing produced normalized 3-dimensional signature maps in both the time and frequency domains. These horizontal profiling maps, corresponding to a 100-Hz-50-kHz passband, demonstrate that a significant anomaly is produced by the cavity as the system is moved across the site. Time-domain maps show nearly a 7-1 change in relative peak-to-peak values, whereas the frequency-domain magnitude response changes as much as 36-1. Results were corroborated using another single-frequency system. These anomalies demonstrate the feasibility of employing induction systems to remotely characterize underground coal-burn cavities filled with conducting fluid.