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We introduce a frequency-domain electromagnetic induction (EMI) instrument for detection and classification of objects with either high ( $\sigma > 10^{5}~\textrm {S/m}$ ) or intermediate ( $1 ) electrical conductivity. While high conductivity metallic targets exhibit a quadrature peak response for frequencies in a traditional EMI regime under 100 kHz, the response of intermediate conductivity objects manifests at higher frequencies, between 100 kHz and 15 MHz. Materials such as carbon fiber or conducting salt solutions exhibit conductivities in this intermediate range and are undetectable by traditional low-frequency EMI instruments. To detect these intermediate conductivity targets, we developed a high-frequency EMI (HFEMI) instrument with a frequency range extended to 15 MHz. The HFEMI instrument requires novel hardware considerations, coil design, and data processing schemes. Most importantly, the wire lengths of transmit and receive coils are shorter than those of traditional frequency EMI sensors, so that the phase on the transmit and receive coils is nearly constant. We present the hardware and software aspects of the HFEMI instrument along with preliminary data, demonstrating its ability to detect intermediate conductive objects.

The U.S. military has developed and currently uses composite material munitions. These composite munitions are typically comprised of carbon fiber and, because of their low electrical conductivity, have a much lower electromagnetic induction signature, which makes them difficult to detect using traditional metal detecting methods. The term intermediate electrically conductive (IEC) is used to describe these lower conductivity materials, with conductivity, σ, typically in the range 10 < σ < 105 S/m. The electromagnetic induction (EMI) relaxation response of carbon fiber munitions peaks in the low megaHertz range (

We present data showing the impact of unmanned aerial systems (UAS) propeller motors electrical noise on the acquisition of electromagnetic induction (EMI) signals. Multiple motor speeds and standoff distances are evaluated. Results indicate an optimum EMI sensor standoff distance from operating UAS motors.

Relative to free-space or non-responsive media, salt water (SW) alters both primary and secondary electromagnetic induction (EMI) fields that pass through it. Effects appear distinctly in the frequency domain (FD), depending on both frequency and sensor-target distance, and may be distinctly quantified. Target signatures are distorted particularly over roughly the upper half log-space of the frequency range where distinctive response patterns may appear. This paper pursues the implications of those distortions when they are translated mathematically to the time domain (TD). Effects of sensor-target standoff, target composition and orientation are pursued. Within configurations of potential interest, SW effects appear strongly in very early time (< 0.1 ms); we investigate the extent to which these effects may spill over further into the early time (ET) range (~ 0.1 ms – 1 ms), which has been important for signal interpretation in sensing on land. Lastly, computations compare the relative magnitude of these SW effects in the secondary field (SF) with those from the SW background itself.

High-frequency electromagnetic induction (HFEMI) extends the established EMI frequency range above 100 kHz to perhaps 20 MHz. In this higher frequency range, less-conductive targets display heretofore unseen responses in their inphase and quadrature components. Improvised explosive device constituent parts, such as carbon rods, small pressure plates, conductivity voids, low metal content mines, and short wires respond to HFEMI but not to traditional EMI. Results from recent testing over mock-ups of less conductive IEDs or their components show distinctive HFEMI responses, suggesting that this new sensing realm could augment the detection and discrimination capability of established EMI technology. The electrical conductivity of soil may contribute, in effect, to the imaginary part of the permittivity of soil and may then, in turn, generate perceptible responses in traditional EMI. In HFEMI, both the real and complete imaginary parts of soil permittivity produce notable effects. Pursuing this, lab tests with tap water and variously saturated Ottawa sand were compared with results from time domain reflectometry.

Processing electromagnetic induction signals from subsurface targets, for purposes of discrimination, requires accurate physical models. To date, successful approaches for on-land cases have entailed advanced modeling of responses by the targets themselves, with quite adequate treatment of instruments as well. Responses from the environment were typically slight and/or were treated very simply. When objects are immersed in saline solutions, however, more sophisticated modeling of the diffusive EMI physics in the environment is required. One needs to account for the response of the environment itself as well as the environment’s frequency and time-dependent effects on both primary and secondary fields, from sensors and targets, respectively. Here we explicate the requisite physics and identify its effects quantitatively via analytical, numerical, and experimental investigations. Results provide a path for addressing the quandaries posed by previous underwater measurements and indicate how the environmental physics may be included in more successful processing.

Thin wires are a critical component of many types of improvised explosive devices. Short wires with lengths on the order of 30 cm to a few meters are difficult to detect using electromagnetic induction metal detectors due to the induction currents having only a small cross-section of the wire to circulate on. Longer wires on the order of tens of meters up to a kilometer are often buried at depths which preclude induction detection. We demonstrate short wire detection and identification through acquiring the electromagnetic induction response at frequencies above the traditional EMI range. In addition, long wire detection and identification is shown through electric field coupling between excitation coils and the long wire itself. We present the relevant physics of detecting both types of wires and experimental and modeling results demonstrating the utility of this high-frequency EMI regime. We present a high-frequency electromagnetic induction instrument utilizing frequencies up to 15 MHz which can detect and classify both short and long wires.

In processing electromagnetic induction (EMI) survey data for UXO discrimination, the nonlinear search for the location of subsurface objects (“targets”) is one of the most onerous and error-producing components. Here we pursue a technique that exploits fundamental analytical electromagnetic relations to extract estimates of a target’s subsurface position. The formulation requires that one be able to estimate secondary field gradients from the data, e.g. by differencing values from multiple, nearby vector receivers. Further, the field in the vicinity of the Rx units is idealized as one structured like that from an infinitesimal dipole responder; this need not restrict the model complexity applied in other aspects of the processing. Once the system is formulated from the data, direct solution of a 3×3 matrix is the only computational burden. Essentially instantaneous results in simulation tests and in processing of some field data locate a hypothetical source point that varies from nominal target position only by a distance on the order of UXO sizes.

Intermediate electrical conductivity (IEC, 100-105 S/m) objects are increasingly important to properly detect and classify. For the US Military, carbon fiber (CF) "smart bomb" unexploded ordnance (UXO) are contaminating training ranges. Home-made explosives (HME) may also fit in this conductivity range. Objects in this conductivity range exhibit characteristic quadrature response peaks at high frequencies (100 kHz-15 MHz). Previous efforts towards electromagnetic induction (EMI) sensing of IEC targets have required single-turn, small-diameter transmitter (Tx) and receiver (Rx) loops. These smaller loops remain electrically short in the high frequency EMI (HFEMI) range (100 kHz-15 MHz), a necessary feature, but provide low signal-to-noise ratio (SNR), especially at low frequencies (1000 Hz-10 kHz). We propose a modification to our pre-production HFEMI instrument which has a hybrid low frequency/high frequency transmit coil. This hybrid system uses many turns in the traditional range, and a single wire turn at HFEMI frequencies, to maximize SNR across a wider EMI band. The turns which are not current carrying in high frequency mode must have negligible inductive coupling to the single current-carrying turn, low enough that any coupling is suitable for background subtraction. This is enforced by mutually disconnecting every turn from every other turn. The instrument uses the same calibration techniques as previously introduced,1 namely background subtraction and ferrite compensation. This paper dis- cusses engineering tradeoffs, compares results to numerical models and actual data from an advanced induction sensor, shows improvement in signal-to-noise ratio (SNR) at traditional EMI frequencies, and shows the same ability to detect IEC targets in the HFEMI band.

Buried threats such as Improvised Explosive Devices (IEDs) and UneXploded Ordnance (UXO) can be composed of different materials including metal, carbon fiber, carbon rods, and nonconducting material such as wood, rubber, fuel oil, and plastic. Electromagnetic induction (EMI) instruments have been traditionally used to detect high electric conductivity discrete targets such as metal UXO. The frequencies used for this EMI regime have typically been less than 100 kHz. To detect intermediate conductivity objects like carbon fiber, higher frequencies up to the low megahertz range are required in order to capture characteristic relaxation responses. Nonconducting voids in an otherwise conducting background medium like soil channel currents around the void. These channeling currents exhibit relaxation responses similar to conducting targets but with a much higher frequency response. Nonconducting plastic landmines can be considered a void plus small metallic parts such as the firing pin, and a characteristic relaxation response due to both the void and the metal parts can be obtained which can reduce false alarms from EMI instruments that detect only the metal. To predict EMI phenomena at frequencies up to 15MHz, we modeled the response of conducting and nonconducting targets using the Method of Auxiliary Sources. Our high-frequency electromagnetic induction (HFEMI) instrument is able to acquire EMI data at frequencies up to that same high limit. Modeled and measured characteristic relaxation signatures compare favorably and indicate new sensing possibilities in a variety of scenarios including the detection of voids and landmines.

Electromagnetic induction (EMI) instruments have been traditionally used to detect high electric conductivity discrete targets such as metal unexploded ordnance (UXO). The frequencies used for this EMI regime have typically been less than 100 kHz. To detect intermediate conductivity objects like carbon fiber, even less conductive saturated salts, and even voids embedded in conducting soils, higher frequencies up to the low megahertz range are required in order to capture characteristic relaxation responses. In this context, nonconducting lastic landmines can be considered a void plus small metallic parts such as the firing pin. To predict EMI phenomena at frequencies up to 15MHz, we modeled the response of conducting and nonconducting targets using the the Method of Auxiliary Sources. Our high-frequency electromagnetic induction (HFEMI) instrument is able to acquire EMI data at frequencies up to that same high limit. Modeled and measured characteristic relaxation signatures compare favorably and indicate new sensing possibilities in a variety of scenarios including the detection of voids and landmines.

Ultrawide band electromagnetic induction (EMI) instruments have been traditionally used to detect high electric conductivity discrete targets such as metal unexploded ordnance. The frequencies used for this EMI regime have typically been less than 100 kHz. To detect intermediate conductivity objects like carbon fiber, even less conductive saturated salts, and even voids embedded in conducting soils, higher frequencies up to the low megahertz range are required in order to capture characteristic responses. To predict EMI phenomena at frequencies up to 15 MHz, we first modeled the response of intermediate conductivity targets using a rigorous, first-principles approach, the Method of Auxiliary Sources. A newly fabricated benchtop high-frequency electromagnetic induction instrument produced EMI data at frequencies up to that same high limit. Modeled and measured characteristic relaxation signatures compare favorably and indicate new sensing possibilities in a variety of scenarios.

The identification and discrimination of unexploded ordnance using low-frequency electromagnetic induction is an expensive and difficult process, typically beset by low data diversity and high positioning uncertainty. In this paper, we present the Man-Portable Vector (MPV) sensor, a new time-domain instrument designed to remedy these shortcomings by measuring all three vector components of the secondary magnetic field at five distinct points around each transmitter location. The MPV also has a laser positioning system that can give its location with millimeter precision. After describing the instrument in detail, we study its performance in various sets of measurements, using the tensor dipole model to analyze the data. We find that the sensor can detect deeply buried targets and identify some standard ordnance items. It can also resolve separate targets in cases where two objects share the field of view and produce overlapping signals. A new incarnation of the MPV, the MPV-II, is in an advanced stage of development.

The simultaneous detection and identification of multiple targets using electromagnetic induction (EMI) time-domain sensors remains a challenge due to the fast decay of the magnetic field with sensor-target distance. For example, the signal from a weak yet shallow target or clutter item can overshadow that from a much larger yet deeper unexploded ordnance (UXO), potentially resulting in erroneous localization and/or identification. We propose, in this paper, a method based on the Gauss-Newton algorithm for the inversion of multiple targets within the field of view of sensors operating at EMI frequencies (tens of hertz to a few hundred kilohertz). In order to minimize the number of unknowns to invert for, the polarizability tensor is written as a time-independent orientation matrix multiplied by a time-dependent diagonal intrinsic polarizability tensor. Similarly, position is supposed to be time independent so that both position and orientation angles are inverted only once using all time channels collected by the instrument. Moreover, using the dipole approximation, we are able to compute the Jacobian in closed form for instruments with either square or circular primary field coils, thus contributing to the speed of the algorithm. Validating results are shown based on the measurement data collected with two EMI sensors on various types of UXO.

A new physics-based expression is presented for determining a buried object’s location, orientation and magnetic polarizibility. The approach assumes the target exhibits a dipolar response and requires only three global values: a magnetic field vector H, a vector potential A and a scalar magnetic potential ψ, all at a single location in space. Among these values, only the scattered magnetic field, H, is measurable with current electromagnetic induction sensors. Therefore, in order to estimate the scattered magnetic scalar and vector potentials from data, a numerical technique called the normalized surface magnetic source (NSMS) method is employed. Originally, in the NSMS model, the scattered magnetic field outside the object is reproduced mathematically by equivalent magnetic charges distributed on a three-dimensional (3-D) closed surface. Here, a two-dimensional (2-D) implementation of the NSMS that uses elementary magnetic dipoles, instead of magnetic charges distributed on a planar surface placed under...

The secondary field produced by 3-D highly permeable and conductive objects is computed in the electromagnetic induction regime, with the purpose of modeling unexploded ordnances (UXOs) and surrounding clutter. The analytical formulation is based on the ellipsoidal coordinate system that is able to model real 3-D geometries as opposed to bodies of revolutions like within a spheroidal approach. At the frequencies of interest (tens of hertz to hundreds of kilohertz), conduction currents in the soil are negligible, and the fields are computed in the magnetoquasistatic regime based on the Laplace equation. Inside the objects, where the wave equation governs the field distribution, the currents are assumed to have a small penetration depth, allowing for the analytical simplification of the field components, which become decoupled at the surface. This approximation, which is valid across the entire frequency spectrum because of the high permeability and conductivity, avoids the necessity of using ellipsoidal wave functions and results in a considerable saving of computational time. Numerical results favorably compare with numerical and experimental data, which proves the usefulness of our method to model UXOs in clutter-contaminated soils. Finally, the optimization approach used to match our numerical predictions with experimental data demonstrates the possibility of remotely inferring the material properties of objects.

This paper presents an application of the normalized surface magnetic charge (NSMC) model to discriminate objects of interest, such as unexploded ordnance (UXO), from innocuous items in cases when UXO electromagnetic induction (EMI) responses are contaminated by signals from other objects. Over the entire EMI spectrum considered here (tens of Hertz up to several hundreds of kHz), the scattered magnetic field outside the object can be produced mathematically by equivalent magnetic charges. The amplitudes of these charges are determined from measurement data and normalized by the excitation field. The model takes into account the scatterer's heterogeneity and near- and far-field effects. For classification algorithms, the frequency spectrum of the total NSMC is proposed and investigated as a discriminant. The NSMC is combined with the differential evolution (DE) algorithm in a two-step inversion procedure. To illustrate the applicability of the DE–NSMC algorithm, blind test data are processed and analyzed for cases in which signals from nearby objects frequently overlap. The method was highly successful in distinguishing UXO from accompanying clutter.

We propose a spheroidal mode approach to characterize the electromagnetic induction (EMI) response of buried objects, assumed to be much more conductive than their environment. Both the excitation and the response are formulated as the linear superpositions of basic spheroidal modes. The scattering coefficients characterize objects, regardless of their geometrical complexity and material inhomogeneity, due to the orthogonality of the spheroidal modes. The ill-conditioning encountered in retrieving the scattering coefficients is dealt with by mode truncation and Tikhonov regularization. The approach is tested for both simulated and measured data, and the retrieval results show encouragingly that only few excitation and response modes effectively represent the EMI response of the objects. The proposed approach is therefore promising in the detection and classification of buried objects

Electromagnetic induction (EMI) is a prominent technique in Unexploded Ord- nance (UXO) detection and discrimination research. Existing idealized forward models for the EMI response can be defeated by both the material and geometrical heterogeneity of realis- tic UXO. We have developed a new, physically complete modeling system referred to as the Standardized Excitations Approach (SEA). The SEA accounts for all the efiects from these het- erogeneities including their interactions within the object, and is applicable in both the near and far flelds. According to the SEA, the excitation fleld is decomposed into fundamental modes, and the response of a given target to each fundamental mode (denoted as a fundamental solution) is obtained beforehand and saved in a library. In this way, the target response to an arbitrary excitation fleld can be calculated via a simple superposition of these fundamental solutions. The model parameters (i.e., the fundamental solutions) of a given object are extracted from a su-ciently detailed set of measurement data. These parameters will be speciflc to each EMI instrument. The parameter extraction process was developed previously for the frequency domain using the GEM-3 EMI instrument. In this paper, we apply this SEA to time domain using the EM-63 instrument as an example. The receiver coil of the EM63 is a 0.5m by 0.5m square loop and can not be approximated by a point receiver. Therefore, in the model, the data is interpreted as the integration of the secondary fleld over the receiver loop. The objects we consider are all Body of Revolution (BOR) type objects. We exploit the fact that the calculated SEA model parameters also exhibit speciflc behavior because the target is a BOR Thus, the algorithm is improved by enforcing symmetric properties and zero total magnetic charge, which makes the algorithm more robust and more e-cient. Preliminary results show that this approach works well for this time domain EMI instrument. After optimization, this model may be fast enough for implementation in inversion processing algorithms.

Electromagnetic induction (EMI) is a popular technique to detect and discriminate buried unexploded ordnance (UXO). However, modeling of the EMI response from many types of UXO is difficult due to the small skin depth of the interior fields. In grid-based numerical methods, meshing the target volume or surface to resolve the skin depth is often highly impractical, yet a failure to do so yields inaccurate results. This paper addresses the problem with a time-domain hybrid technique based on thin-skin approximation (TSA) that is very accurate for small skin depths. The TSA method is applied to axisymmetric problems and is shown to be both fast and accurate when the skin depth is small. The method is compared with analytical results, and excellent agreement is obtained. For magnetic materials (such as steel), the TSA method is accurate for the complete time-domain EMI response. In such cases, the TSA method provides an improved accuracy along with an order-of-magnitude reduction in CPU time compared to a dense-mesh finite-element method (FEM). For nonmagnetic materials, the TSA loses accuracy as time progresses and must be combined with a coarse-mesh FEM. In such cases, the combined method still provides greater accuracy with comparable CPU time

In practice, most signal processing strategies for discrimination of buried objects are clutter limited. This applies even to discrimination of shallow sizable metallic objects, such as unexploded ordnance (UXO), which are to be found predominantly in the top meter of soil. The environment typically features widespread metallic clutter from detonated ordnance or other sources. Such fragments can be numerous and are often shallower than the objects of interest. Currently, the preeminent remote sensing mode for buried UXO is ultrawideband electromagnetic induction (EMI), operating over part or all of the band from some tens of hertz up to perhaps hundreds of kilohertz. Particularly because EMI fields fall off sharply with range, signals from shallow clutter may be relatively strong and can easily obscure essential scatterer signatures. To treat this, a rational theory of EMI scattering from widespread metallic clutter is formulated and tested. For dense, well-distributed clutter, analytical rules are derived for dependence of signal strength on sensor elevation, under various fundamental excitation types. For more erratic, sparse clutter distributions, signal statistics from Monte Carlo simulations show patterns like those from the analytical rules. The dependence of clutter signal magnitude on antenna elevation is determined for both thin surface layers and for volume layers of widespread small items, and for both dense and sparse clutter distributions. These are contrasted with the patterns expected from single, larger, discrete objects of interest, and the contrast is exploited in discrimination exercises for the screening problem. For sparse clutter distributions, results from inversion processing formulations that account for the patterns of clutter statistics are compared to simple least squares treatments.

Current idealized forward models for electromagnetic induction (EMI) response can be defeated by the characteristic material and geometrical heterogeneity of realistic unexploded ordnance (UXO). A new, physically complete modeling system was developed that includes all effects of these heterogeneities and their interactions within the object, in both near and far fields. The model is fast enough for implementation in inversion processing algorithms. A method is demonstrated for extracting the model parameters by straightforward processing of data from a defined measurement protocol. Depending on the EMI sensor used for measurements, the process of inferring model parameters is more or less ill-posed. More complete data can alleviate the problem. For a given set of data, special numerical treatment is introduced to take the best advantage of the data and obtain reliable model parameters. The resulting fast model is implemented in a pattern matching treatment of measurements by which signals from a UXO are identified within a series of those from unknown targets. Preliminary results show that this fast model is promising for use in processing of this kind. The inherent difficulties of target identification are examined, and solutions for resolving these difficulties are discussed.

We use a hybrid model including asymptotic expressions of the spheroidal wave functions (SWFs) to obtain a reliable broadband solution for the electromagnetic induction (EMI) response from a conducting and permeable spheroid. We obtain this broadband response, valid in the magnetoquasistatic regime from zero to hundreds of kilohertz, by combining three different techniques, each applicable over a different frequency range. At low frequencies, the exact analytical solution is used. At midrange frequencies, asymptotic expressions for the angular and radial SWFs are incorporated into the exact solution in order to maintain a stable solution for the induced magnetic field. At higher frequencies, a small penetration approximation (SPA) solution is used when the SPA solution approaches the asymptotically assisted solution to within some predefined tolerance. Validation of this combined technique is accomplished through the comparison of the induced magnetic field predicted by our model to both a finite element/boundary integral (FE-BI) numerical solution and experimental data from various spheroids taken by an ultrawideband EMI instrument.

Operating in the magnetoquasistatic regime (a few hertz to perhaps a few 100 kHz), electromagnetic induction (EMI) sensing has recently emerged as one of the most promising avenues for discrimination of subsurface metallic objects, e.g., unexploded ordnance. The technique of thin-skin approximation (TSA) was devised to deal with numerical problems caused by the rapid decay of fields beneath the scatterer's surface. The rather nonintuitively broad applicability and specific error patterns of the TSA formulation are explained here by theoretical analysis based on analytical solutions and approximate Monte Carlo simulation. In the limiting case of infinitesimal skin depth (EMI perfect reflection), the scatterer aspect ratio (AR) is inferred without regard to metal type. Alternatively, the AR of some homogeneous magnetic objects is inferred from the pattern of transverse to axial response ratio over the entire EMI ultrawideband. Use of the method in inversions for electromagnetic parameters reveals fundamental nonuniqueness problems and shows their basis, which is not dependent on the method of forward solution.

Operating as low as tens of hertz and as high as hundreds of kilohertz, new broadband electromagnetic induction (EMI) sensors have shown promise for classification of unseen buried metallic objects. The three-dimensional (3-D) and bodies-of-revolution (BOR) numerical studies reported here are designed to explain key scattering sensitivities that may either be useful in or may limit object classification capability. The target is excited either by a spatially uniform oscillating primary magnetic field or by the oscillating field from a loop antenna. The problem is formulated in terms of Poison's equation for scalar potential outside the object, where conductivity and electric field values are low and consequent conduction currents are generally negligible. The Helmholtz equation for vector potential applies inside the highly conducting and permeable object. In both regions, the electromagnetic phenomena of interest are magneto-quasi-static (MQS). The simulation algorithm uses the method of auxiliary sources (MAS), with auxiliary magnetic charges and auxiliary magnetic current elements distributed on auxiliary surfaces. These surfaces generally conform to but do not coincide with physical surfaces, providing extraordinarily efficient and accurate 3-D solutions. Comparisons to available analytical solutions and experimental data validate the solutions. The simulations and data illuminate broadband MQS scattering phenomenology for both magnetic and nonmagnetic metallic objects. Distinctive sensitivities are shown and signature effects analyzed relative to the scatterer's shape and aspect ratio, orientation, sharp points and edges, finite wall thickness in hollow bodies, and compound structure in which a geometrically complex body consists of a number of distinct sections, e.g., fins.

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.

The problem of numerical modeling of electromagnetic induction (EMI) responses by metallic objects is complicated by the fact that transmitted fields may penetrate the target, but will often only do so slightly. The effect cannot be ignored, yet it is often grossly impractical to discretize the entire surface or volume of a target in space increments only on the order of a fraction of the skin depth. To deal with this problem, we retain a simple integral equation formulation in scalar potential for the region outside the target, where magnetic fields are quasi-static and irrotational. Within the target we apply only the divergence relation, /spl nabla//spl middot/H = 0. When the skin depth is small relative to the radius of curvature of the target (e.g.

High frequency (>100 kHz) electromagnetic induction (HFEMI) sensing phenomena are investigated for non- metallic ordnances detection and discrimination. HFEMI responses are studied using numerical and experimental data. The numerical modeling is done via the method of auxiliary sources, and data are collected using a new HEMI system, that has been developed at our lab. The comparisons between modeled and actual data are illustrated for a non-metallic 105 mm projectile.

The Portable Decoupled Electromagnetic Induction Sensor (Pedemis) is a new instrument designed to provide diverse, high-quality data for detection and discrimination of unexploded ordnance in rocky, treed, or otherwise forbidding terrain. It consists of a square array of nine transmitters and a similar arrangement of receivers that measure all three vector components of the time-dependent magnetic field at nine different locations. The receiver assembly can be fixed to the transmitters or detached from them for enhanced flexibility and convenience. The latter mode requires a positioning system that finds the location of the receivers with respect to the transmitters at any time without hampering portability or requiring communication with outside agents (which may be precluded by field conditions). The current system examines the primary field during the transmitters’ on-time phase and optimizes to find the location at which it is most likely to obtain the combination of measured values. We have developed an algorithm that computes mutual inductances analytically and exploits their geometric information to predict location. The method does full justice to Faraday’s Law from the start and incorporates the fine structure of both transmitters and receivers; it is exact and involves only elementary functions, making it unnecessary to set up and monitor approximations and guaranteeing robustness and stability everywhere; it uses a fraction of the memory and is orders-of-magnitude faster than methods based on numerical quadrature. We have tested the algorithm on the current Pedemis prototype and have obtained encouraging results which we summarize in this paper.

This paper details methods for automatic classification of Unexploded Ordnance (UXO) as applied to sensor data from the Spencer Range live site. The Spencer Range is a former military weapons range in Spencer, Tennessee. Electromagnetic Induction (EMI) sensing is carried out using the 5x5 Time-domain Electromagnetic Multi-sensor Towed Array Detection System (5x5 TEMTADS), which has 25 receivers and 25 co-located transmitters. Every transmitter is activated sequentially, each followed by measuring the magnetic field in all 25 receivers, from 100 microseconds to 25 milliseconds. From these data target extrinsic and intrinsic parameters are extracted using the Differential Evolution (DE) algorithm and the Ortho-Normalized Volume Magnetic Source (ONVMS) algorithms, respectively. Namely, the inversion provides x, y, and z locations and a time series of the total ONVMS principal eigenvalues, which are intrinsic properties of the objects. The eigenvalues are fit to a power-decay empirical model, the Pasion-Oldenburg model, providing 3 coefficients (k, b, and g) for each object. The objects are grouped geometrically into variably-sized clusters, in the k-b-g space, using clustering algorithms. Clusters matching a priori characteristics are identified as Targets of Interest (TOI), and larger clusters are automatically subclustered. Ground Truths (GT) at the center of each class are requested, and probability density functions are created for clusters that have centroid TOI using a Gaussian Mixture Model (GMM). The probability functions are applied to all remaining anomalies. All objects of UXO probability higher than a chosen threshold are placed in a ranked dig list. This prioritized list is scored and the results are demonstrated and analyzed.

This study is designed to illustrate the discrimination performance at two UXO active sites (Oklahoma’s Fort Sill and the Massachusetts Military Reservation) of a set of advanced electromagnetic induction (EMI) inversion/discrimination models which include the orthonormalized volume magnetic source (ONVMS), joint diagonalization (JD), and differential evolution (DE) approaches and whose power and flexibility greatly exceed those of the simple dipole model. The Fort Sill site is highly contaminated by a mix of the following types of munitions: 37-mm target practice tracers, 60-mm illumination mortars, 75-mm and 4.5′′ projectiles, 3.5′′, 2.36′′, and LAAW rockets, antitank mine fuzes with and without hex nuts, practice MK2 and M67 grenades, 2.5′′ ballistic windshields, M2A1-mines with/without bases, M19-14 time fuzes, and 40-mm practice grenades with/without cartridges. The site at the MMR site contains targets of yet different sizes. In this work we apply our models to EMI data collected using the MetalMapper (MM) and 2 × 2 TEMTADS sensors. The data for each anomaly are inverted to extract estimates of the extrinsic and intrinsic parameters associated with each buried target. (The latter include the total volume magnetic source or NVMS, which relates to size, shape, and material properties; the former includes location, depth, and orientation). The estimated intrinsic parameters are then used for classification performed via library matching and the use of statistical classification algorithms; this process yielded prioritized dig-lists that were submitted to the Institute for Defense Analyses (IDA) for independent scoring. The models’ classification performance is illustrated and assessed based on these independent evaluations.

Pedemis (PortablE Decoupled Electromagnetic Induction Sensor) is a time-domain man-portable electromagnetic induction (EMI) instrument with the intended purpose of improving the detection and classification of UneXploded Ordnance (UXO). Pedemis sports nine coplanar transmitters (the Tx assembly) and nine triaxial receivers held in a fixed geometry with respect to each other (the Rx assembly) but with that Rx assembly physically decoupled from the Tx assembly allowing flexible data acquisition modes and deployment options. Such flexibility is expected to be instrumental in non-trivial terrains exhibiting either an abundant vegetation or being highly contaminated by large or dense clutter. Before validating the sensor in such challenging configurations, however, Pedemis was taken to Aberdeen Proving Ground, MD, for its first test site validation. We describe Pedemis, including its operation and data acquisition modes along with our Aberdeen Proving Ground results.

Current electromagnetic induction (EMI) sensors of the kind used to discriminate buried unexploded orndance (UXO) can detect targets down to a depth limited by the geometric size of the transmitter (Tx) coils, the amplitudes of the transmitting currents, and the noise floor of the receivers (Rx). The last two factors are not independent: for example, one cannot detect a deeply buried target simply by increasing the amplitude of the Tx current, since this also increases the noise and thus does not improve the SNR. The problem could in principle be overcome by increasing the size of the Tx coils and thus their moment. Current multi-transmitter instruments such as the TEMTADS sensor array can be electronically tweaked to provide a big Tx moment: they can be modified to transmit signals from two, three or more Tx coils simultaneously. We investigate the possibility of enhancing the deep-target detection capability of TEMTADS by exploring different combinations of Tx coils. We model different multi-Tx combinations within TEMTADS using a full-3D EMI solver based on the method of auxiliary sources (MAS).We determine the feasibility of honing these combinations for enhanced detection and discrimination of deep targets. We investigate how to improve the spatial resolution and focusing properties of the primary magnetic field by electronically adjusting the currents of the transmitters. We apply our findings to data taken at different UXO live sites.

Pedemis (PortablE Decoupled Electromagnetic Induction Sensor) is a time-domain handheld electromagnetic induction (EMI) instrument with the intended purpose of improving the detection and classification of UneXploded Ordnance (UXO). Pedemis sports nine coplanar transmitters (the Tx assembly) and nine triaxial receivers held in a fixed geometry with respect to each other (the Rx assembly) but with that Rx assembly physically decoupled from the Tx assembly allowing flexible data acquisition modes and deployment options. The data acquisition (DAQ) electronics consists of the National Instruments (NI) cRIO platform which is much lighter and more energy efficient that prior DAQ platforms. Pedemis has successfully acquired initial data, and inversion of the data acquired during these initial tests has yielded satisfactory polarizabilities of a spherical target. In addition, precise positioning of the Rx assembly has been achieved via position inversion algorithms based solely on the data acquired from the receivers during the "on-time" of the primary field. Pedemis has been designed to be a flexible yet user friendly EMI instrument that can survey, detect and classify targets in a one pass solution. In this paper, the Pedemis instrument is introduced along with its operation protocols, initial data results, and current status.

In this paper we employ advanced electromagnetic induction models to resolve multiple targets with overlapping EMI signals-i.e. to discriminate objects of interest, such as unexploded ordnance (UXO), from innocuous items. The models include a) a joint diagonalization (JD) technique that takes data from next-generation EMI sensors and uses the eigenvalues of the multistatic response matrix to estimate the number of potential targets, and b) the orthonormalized volume magnetic source (ONVMS) model, a physically complete, fast, and accurate forward model whose representation of a target's intrinsic EMI response is used to extract classification parameters. In the given approach the overall EMI inversion and classification problem proceeds as follows: first, the JD is applied to the data and the number of targets is estimated; once this is known, the ONVMS is combined with an optimization technique to yield the location and orientation of each buried object, as well as the amplitude of its ONVMS. Finally, a total ONVMS is calculated for each object and used as a discriminant to distinguish between UXO and non-UXO items and between different kinds of UXO. We illustrate the applicability of our multi-target analysis technique by using it on several teststand and live-site datasets collected with the TEMTADS sensor array. We end by demonstrating the superior performance of the ONVMS by applying it to multi-target blind-test data compiled at the Aberdeen Proving Ground test-stand facility.

In this paper we present the inversion and classification performance of the advanced EMI inversion, processing and discrimination schemes developed by our group when applied to the ESTCP Live-Site UXO Discrimination Study carried out at the former Camp Butner in North Carolina. The advanced models combine: 1) the joint diagonalization (JD) algorithm to estimate the number of potential anomalies from the measured data without inversion, 2) the ortho-normalized volume magnetic source (ONVMS) to represent targets' EMI responses and extract their intrinsic "feature vectors," and 3) the Gaussian mixture algorithm to classify buried objects as targets of interest or not starting from the extracted discrimination features. The studies are conducted using cued datasets collected with the next-generation TEMTADS and MetalMapper (MM) sensor systems. For the cued TEMTADS datasets we first estimate the data quality and the number of targets contributing to each signal using the JD technique. Once we know the number of targets we proceed to invert the data using a standard non-linear optimization technique in order to determine intrinsic parameters such as the total ONVMS for each potential target. Finally we classify the targets using a library-matching technique. The MetalMapper data are all inverted as multi-target scenarios, and the resulting intrinsic parameters are grouped using an unsupervised Gaussian mixture approach. The potential targets of interest are a 37-mm projectile, an M48 fuze, and a 105-mm projectile. During the analysis we requested the ground truth for a few selected anomalies to assist in the classification task. Our results were scored independently by the Institute for Defense Analyses, who revealed that our advanced models produce superb classification when starting from either TEMTADS or MM cued datasets.

Dynamic data from the MetalMapper electromagnetic induction sensor are analyzed using a fast inversion algorithm in order to obtain position information of buried anomalies. After validating the algorithm by comparing static and dynamic inversions from reference measurements at Camp San Luis Obispo, the algorithm is applied to realistic dynamic measurements from Camp Butner. A sequence of 939 data points are inverted as the MetalMapper travels along a calibration lane, flagging a few positions as corresponding to buried anomalies. An a posteriori comparison with field plots reveals a good agreement between the flagged positions and the field peak values, suggesting the efficacy of the algorithm at detecting a large variety of anomalies from dynamic data.

The Man-Portable Vector (MPV) electromagnetic induction sensor has proved its worth and flexibility as a tool for identification and discrimination of unexploded ordnance (UXO). TheMPV allows remediation work in treed and rough terrains where other instruments cannot be deployed; it can work in survey mode and in a static mode for close interrogation of anomalies. By measuring the three components of the secondary field at five different locations, the MPV provides diverse time-domain data of high quality. TheMPV is currently being upgraded, streamlined, and enhanced to make it more practical and serviceable. The new sensor, dubbedMPV-II, has a smaller head and lighter components for better portability. The original laser positioning system has been replaced with one that uses the transmitter coil as a beacon. The receivers have been placed in a configuration that permits experimental computation of field gradients. In this work, after introducing the new sensor, we present the results of several identification/discrimination experiments using data provided by the MPV-II and digested using a fast and accurate new implementation of the dipole model. The model performs a nonlinear search for the location of a responding target, at each step carrying out a simultaneous linear least-squares inversion for the principal polarizabilities at all time gates and for the orientation of the target. We find that the MPV-II can identify standard-issue UXO, even in cases where there are two targets in its field of view, and can discriminate them from clutter.

The physically complete Normalized Surface Magnetic Source (NSMS) model and a variant of the simple dipole model are applied to new-generation electromagnetic induction (EMI) data. The main objective is to assess the NSMS and dipole models' capabilities to discriminate between UXO and clutter starting from scattered EMI signals. The discrimination contains two sets of parameters: (1) intrinsic parameters associated with the size, shape, and material composition of the target; and (2) extrinsic parameters related to the orientation and location of the anomaly. To discriminate UXO from clutter a mathematical model is fit to the geophysical data, after which both intrinsic and extrinsic parameters are extracted using an optimization technique. The inverted intrinsic parameters thus found are used to isolate objects of interest from non-hazardous items. The discrimination performance depends significantly on the mathematical model. In this work we present results of applying the single dipole, multi-dipole, and NSMS models to single- and multi-axis sensor data produced by new-generation EMI instruments such as MPV, TEMTADS, and MetalMapper, all of which are are time-domain systems. The MPV has a single transmitter and five tri-axial receivers, the TEMTADS array is a towed system featuring 25 transmitter/receiver pairs, and MetalMapper contains three rectangular transmitters and five tri-axial receivers distributed on a plane. The inversion and discrimination performance of the NSMS and single-dipole models are illustrated for the high-quality, well-located EMI data produced by these instruments. Specifically, we present comparisons between inverted intrinsic and extrinsic parameters, as determined from each model and compared with the ground truth.

In subsurface UXO sensing, single field (SF) data arises when an excitation field produces a single, spatially distributed response field that is sampled from a number of different locations. Measurements from traditional magnetometers furnish such data, for which the essentially invariant excitation is the earth's magnetic field. Upward continuation (UC) of SF data allows one to calculate signals that would be received at a higher elevation above the ground without actually raising the receiver. This is done without having to solve for the actual target characteristics or location. The technique is designed to smooth out the perturbations from irregularities and near-surface clutter. Applied recently to broader band electromagnetic induction (EMI) data of the the GAP SAM system, UC has shown distinct benefits in suppressing the strength of near surface clutter signals relative to those from a deeper UXO. Here we investigate possible application to the TEMTADS sensor. Preliminary results suggest that here too the method may bring out the signal of an underlying larger UXO relative to discrete clutter or smaller shallowers items, thereby aiding discrimination. In future work resolution issues must be addressed.

The detection of unexploded ordnance (UXO) in the electromagnetic induction regime often suffers from a low signal to noise ratio due to the strong decay of the magnetic field. As a result, a deep UXO may be overshadowed by smaller yet shallower metal items which render the classification of the main target challenging. It is therefore desirable to have the ability to model the various sources of noise and to include them in a detection algorithm. Toward this effect, we investigate here Kalman and extended Kalman filters for the inversion of UXO polarizabilities and positions, respectively, within a dipole model approximation. Inherent to the method, our analysis is based on the assumption of Gaussian noise distribution, which is often reasonable. Results are shown on both synthetic and TEMTADS data which have been purposely corrupted with noise. In particular, the situation of a main target in the presence of dense clutter is investigated, whereby the clutter is composed of 16 nosepieces buried close to the sensor.

We assess the noise level caused by marine environments in underwater UXO discrimination studies. Underwater UXO detection and discrimination is subject to additional noise sources, which are not present in land-based scenarios. Particularly, we study the effects of water surface roughness on the diffusion of EMI (electromagnetic induction) fields through the air-water interface and the interaction effects between an underwater conducting object and its surrounding conductive medium. Numerical simulations are conducted using the 3-dimensional setup of the Method of Auxiliary Sources suitable for low-frequency regime. Water surface roughness is modeled as an interference pattern between a finite number of surface waves with varying amplitudes, wavelengths and propagation directions. The results indicate that the perturbations in diffused and scattered EMI fields due to water surface roughness are negligible (although they depend on the shape of water surface) and that these perturbations decay with distance from the interface. Thus, the conducting water body may be assumed to represent a half-space in subsequent calculations for UXO detection. Finally, it is shown that there is some interaction between a conducting object and its surrounding conductive environment for frequencies above 100 kHz. This interaction is attenuated if the object is surrounded by an insulating shell, but is amplified if the shell is conducting. This non-negligible effect needs to be taken into account for the purposes of UXO detection and discrimination.

The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP) method to locate targets, the normalized surface magnetic source (NSMS) model to characterize them, and a support vector machine (SVM) to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.

Clutter is the bane of electromagnetic induction (EMI) surveying for subsurface unexploded ordnance (UXO) under realistic circumstances. Relatively small near-surface metallic items can still produce significant signals simply because they are much closer to the sensor than the larger underlying target of interest. Based on measured, fully multi-static, scalar data at some typical elevation above the ground, one may infer a surface layer of equivalent sources that will produce that data. Without having to locate or characterize the actual targets, one can use these equivalent sources to predict complete vector field data that would be obtained at any elevation equal to or greater than that of the original data. Such computational upward continuation (UC) of signals successfully suppressed clutter in field data. This was even the case when the local clutter signal was significantly stronger than that of the broader underlying UXO response and was embedded directly within it. The success of the approach is directly tied to the fact that it relies on the governing physics.

Recently, several sensor technologies, such as magnetometers (total-field and gradiometers) and various types of timedomain and frequency-domain electromagnetic induction (EMI) sensors have been developed and applied successfully to land-based subsurface unexploded ordnance (UXO) detection and mapping. Current researchers of underwater UXO detection commonly apply land-based UXO detection technologies directly to underwater scenarios. Since the electric conductivity of water is much higher than that of soil, an object's EMI response underwater should be different than in a dry environment because inside the conducting water low-frequency electromagnetic signals change both in magnitude and phase, particularly at high frequencies where induction numbers (i.e., wavenumbers) are significantly high. In order to fully explore the capabilities and limitations of land-based EMI sensors for underwater UXO detection and discrimination, in this paper we assess the applicability of current EMI forward models by investigating how the electromagnetic parameters of seawater affect the performance of state-of-the-art EMI sensors. The studies are conducted using the Generalized Standardized Excitation Approach. Objects' locations are inverted for using a reduced version of the HAP technique that combines the magnetic field and its gradient. Particular attention is given to understanding how seawater EM parameters or a multilayer conductive background change objects' EMI responses and affect the UXO discrimination process.

The detection of unexploded ordnance (UXO) in the presence of a discrete and large clutter is here investigated in the electromagnetic induction regime using a Newton method with no a priori information on the position or the strength of each object. The problem is formulated as a cost-function minimization on the difference in magnetic fields between the measured or synthetic data and their corresponding predictions. Both a bistatic and a monostatic operating modes are considered and applied to various geometrical configurations such as targets in close proximity or on top of each other. Measurement data from the TEMTADS sensor in a two-object configuration are also analyzed. The results illustrate the accuracy of the method in many situations, but also point out at some current limitations for which further improvements are suggested.

In this paper, a new physics-based approach for estimating a buried object's location and orientation is combined with the normalized surface magnetic source (NSMS) model to analyze high-quality, high-density multiaxis data provided by the Man-Portable Vector (MPV) time domain (TD) sensor. The NSMS is a very simple and robust technique for predicting the EMI responses of various objects. It is applicable to any combination of magnetic or electromagnetic induction data for any arbitrary homogeneous or heterogeneous 3D object or set of objects. The physics-based approach to estimate location assumes that the target exhibits a dipolar response and uses only two global values, the magnetic field vector H and the scalar magnetic potential psi, reconstructed at a set of points in space. To demonstrate the applicability of the NSMS, we first compare its predictions with dynamic MPV-TD measurements and then present the results of a blind-test analysis using multiaxis static MPV-TD data sets.

Recently different time domain (TD) electromagnetic induction sensors have been developed and tested for UXO detection and discrimination. These sensors produce well-locat ed, multi-axis, high-density data. One of such sensors is the Man Portable Vector (MPV) TD sensor build by GG this is the most time consuming part of the UXO classification process. In order to avoid solving a traditional ill-posed inverse scattering problem, here we adapt to TD-MPV data a recently develo ped physics-based approach [1], called (HAP), to estimate a buried object’s location and orientation. The approach as sumes the target exhibits a dipolar response and uses only three global values: (1) the magnetic field vector H , (2) the vector potential A, and (3) the scalar magnetic potential at a point in space. Of these three global values only the flux of the H field is measurable by the MPV sens or. However, the vector and scalar magnetic potentials can be recovered from measured magnetic field data using a 2D NSMS approach. To demonstrate the applicability of the NSMS and HAP techniques we report the results of a blind-test analysis using multi-axis TD MPV data collected at the U.S. Army’s ERDC UXO test site. Keywords: TD MPV, EMI Sensing, NSMS, Scalar and Vector potentials, Inversion, Discrimination 1. INTRODUCTION The detection and remediation of UXO on military ranges still continues to be the number one military environmental problem. A wide range of different sensing technologies, with novel waveforms, multi-axis or vector receivers, tensor magnetic or electromagnetic induction data, or any combination of magnetic and EMI data, are currently being used or developed for detecting and discri minating UXO [2]-[11]. Among these emerging technologies is the new man portable vector time-domain (MPV TD) sensor [4], [10], which has five tri-axial cubic receivers located around two 75-cm diameter transmitter coils. The positions of the transmitter coils are determined using a laser positioning system. The combination of spatial diversity in the measurements, including elevation changes, with full vector definition of signals from a variety of observation points, over an extremely wide time range, offers unprecedented data quality for discrimination-processing algorithms. To take advantage of the data diversity that this instrument provides, new EMI forward models and data interpretation and processing schemes have been recently developed as well.

There are approximately one million acres of underwater lands at Department of Defense (DOD) and Department of Energy (DOE) sites that are highly contaminated with unexploded ordnance (UXO) and land mines. The detection and disposal of Underwater Military Munitions are more expensive than excavating the same targets on land. Electromagnetic induction (EMI) sensing has emerged as one of the most promising technologies for underwater detection. In order to explore the full potential of various EMI sensing technologies for underwater detection and discrimination, to achieve a high (~100%) probability of detection, and to distinguish UXO from non-UXO items accurately and reliably, first the underlying physics of EM scattering phenomena in underwater environments needs to be investigated in great detail. This can be achieved by using an accurate 3D numerical code, such as the combined method of auxiliary sources and thin skin depth approximation (MAS/TSA), the pseudospectral time-domain technique, finite element methods or other approaches. This paper utilizes the combined MAS/TSA, originally developed for detection and discrimination of highly conducting and permeable metallic objects placed in an environment with zero or negligible conductivity. Here, first the theoretical basis of the MAS/TSA is presented for metallic objects placed in an electrically conductive environment. Then numerical experiments are conducted for homogeneous targets of canonical (spheroidal) shapes subject to frequency- or time-domain illumination. The results illustrate coupling effects between the object and its surrounding conductive medium, particularly at high frequencies (early times for time-domain sensors), and the way this coupling depends on the distance between the sensor and the object's center.

The modeling of unexploded ordnances in the electromagnetic induction regime (from tens of hertz to a few kilohertz) is carried out in the ellipsoidal coordinate system, thus presenting an important generalization of the existing dipole model or spheroidal approach. Ellipsoidal geometries are of interest because of their ability to more accurately model real 3D objects while retaining the appeal of an analytical solution. Computation speed is minimized here by assuming objects of high permeability and conductivity, allowing for the use of the small penetration approximation, and avoiding the necessity of solving the wave equation using ellipsoidal wave functions inside the object. Simulations are compared with both existing numerical solutions and with measured data on fabricated ellipsoids, and prove the validity of our model.