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Depleted uranium (DU) is a byproduct of the uranium enrichment process and contains less than 0.3 % of the radioactive U-235 isotope. Since, the natural uranium has about 0.72 % of the uranium U-235 isotope, the enrichment produces large quantities of low-level radioactive DU. The non-fissile uranium U-238 isotope constitutes the main component of DU and makes it very dense. With 19.1 g/cm 3 density, the DU is about 68.4 % denser than lead. Because of its high density, the DU has been used for as armor-piercing penetrators by the U.S. army. There are at least 30 facilities where munitions containing DU have been evaluated or used for training. These evaluation studies have been conducted with and without catch-boxes and have left a legacy of DU contamination. Thus, there are needs for rapid and cost-effective approaches to detect and locate subsurface DU munitions and to assess large contaminated areas. In this paper, a new ultra-wideband (from 10s of Hertz up to 15 Megahertz) geophysical instrument is evaluated for sensing subsurface DU munitions and DU materials related to contaminations in soil. Namely, full electromagnetic induction (EMI) responses are investigated using computational and experimental data for a DU rod, dart, and three samples of Yuma Proving Ground (YPG) soils. Numerical data are obtained via the full 3D EMI solver based on the method of auxiliary sources. The EMI signals sensitivity with respect to DU size, orientations, and material composition are illustrated and analyzed. Comparisons between computational and experimental studies are demonstrated. The studies show that the new ultra-wideband EMI sensor measures the complete polarization relaxation response from the DU rod and dart, and is able to sense relative DU contamination levels in soil.

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.

This paper presents the current status of ASPIICS, a solar coronagraph that is the primary payload of ESA’s formation flying in-orbit demonstration mission PROBA-3. The “sonic region” of the Sun corona remains extremely difficult to observe with spatial resolution and sensitivity sufficient to understand the fine scale phenomena that govern the quiescent solar corona, as well as phenomena that lead to coronal mass ejections (CMEs), which influence space weather. Improvement on this front requires eclipse-like conditions over long observation times. The space-borne coronagraphs flown so far provided a continuous coverage of the external parts of the corona but their over-occulting system did not permit to analyse the part of the white-light corona where the main coronal mass is concentrated. The PROBA-3 Coronagraph System, also known as ASPIICS (Association of Spacecraft for Polarimetric and Imaging Investigation of the Corona of the Sun) is designed as a classical externally occulted Lyot coronagraph but it takes advantage of the opportunity to place the external occulter on a companion spacecraft, about 150m apart, to perform high resolution imaging of the inner corona of the Sun as close as ~1.1 solar radii. The images will be tiled and compressed on board in an FPGA before being down-linked to ground for scientific analyses. ASPIICS is built by a large European consortium including about 20 partners from 7 countries under the auspices of the European Space Agency. This paper is reviewing the recent development status of the ASPIICS instrument as it is approaching CDR.

Formation flying is one of the most promising techniques for the future of astronomy and astrophysics from the space. The capabilities of the rockets strongly affect the dimensions and the weights of telescopes and instrumentation to be launched. Telescopes composed by several smallest satellites in formation flying, could be the key for build big space telescopes. With this aim, the ESA PROBA-3 mission will demonstrate the capabilities of this technology, maintaining two satellites aligned within 1 mm (longitudinal) when the nominal distance between the two is of around 144m. The scientific objective of the mission is the observation of the solar corona down to 1.08 solar radii. The Coronagraph Spacecraft (CSC) will observe the Sun, when the second spacecraft, the Occulter Spacecraft (OSC) will work as an external occulter, eclipsing to the CSC the sun disk. The finest metrology sub-systems, the Shadow Position Sensors (SPS) and the Occulter Position Sensor Emitters (OPSE) identifying respectively the CSC-Sun axis and the formation flying (i.e., CSC-OSC) axis will be considered here. In particular, this paper is dedicated to the test-bed for the characterization, the performance analysis and the algorithms capabilities analysis of the both the metrology subsystems. The test-bed is able to simulate the different flight conditions of the two spacecraft and will give the opportunity to check the response of the subsystems in the conditions as close as possible to the flight ones.

The "sonic region" of the Sun corona remains extremely difficult to observe with spatial resolution and sensitivity sufficient to understand the fine scale phenomena that govern the quiescent solar corona, as well as phenomena that lead to coronal mass ejections (CMEs), which influence space weather. Improvement on this front requires eclipse-like conditions over long observation times. The space-borne coronagraphs flown so far provided a continuous coverage of the external parts of the corona but their over-occulting system did not permit to analyse the part of the white-light corona where the main coronal mass is concentrated. The proposed PROBA-3 Coronagraph System, also known as ASPIICS (Association of Spacecraft for Polarimetric and Imaging Investigation of the Corona of the Sun), with its novel design, will be the first space coronagraph to cover the range of radial distances between ~1.08 and 3 solar radii where the magnetic field plays a crucial role in the coronal dynamics, thus providing continuous observational conditions very close to those during a total solar eclipse. PROBA-3 is first a mission devoted to the in-orbit demonstration of precise formation flying techniques and technologies for future European missions, which will fly ASPIICS as primary payload. The instrument is distributed over two satellites flying in formation (approx. 150m apart) to form a giant coronagraph capable of producing a nearly perfect eclipse allowing observing the sun corona closer to the rim than ever before. The coronagraph instrument is developed by a large European consortium including about 20 partners from 7 countries under the auspices of the European Space Agency. This paper is reviewing the recent improvements and design updates of the ASPIICS instrument as it is stepping into the detailed design phase.

Packaging can have a significant impact on the performance characteristics of Silicon Photomultipliers (SiPM) sensors as well as having an impact on reliability and yield. To provide the highest performance possible, SensL have recently developed and tested a surface mount, through silicon via (TSV) package that provides high array fill factor, high photon detection efficiency (PDE) and magnetic resonance imaging (MRI) system compatibility. The PDE of TSV packaged sensors will be shown to be the highest when compared to traditional SiPM package types. In addition the PDE in the UV and blue region will be shown to approach that of unprotected bare die. Additionally, the TSV package has minimal deadspace outside of the active area which will be shown to allow close packing when used in a sensor array. It will be shown that arrays of TSV sensors have the highest fill factor currently possible when creating arrays from singulated die. Additionally, it will be shown that TSV parts are non-magnetic and results of images taken with the TSV SiPM in a 3 Tesla magnetic resonance imaging (MRI) system will be shown to have no impact on the MRI system.

SensL C-Series Silicon Photomultiplier (SiPM) sensors are fabricated in a high-volume CMOS foundry to a custom SensL process, and packaged as a reflow solderable surface mount device. Advances in SiPM production have resulted in significant improvement in PDE, dark current as well as tighter breakdown voltage uniformity for the C-Series SiPM sensors. The SiPM are fabricated with a shallow P-on-N junction optimized for the detection of shorter wavelength photons, with a peak PDE of 41% at 420nm and excellent sensitivity extending to wavelengths

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.

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.

Advanced electromagnetic induction (EMI) sensors currently feature multi-axis illumination of targets and tri-axial vector sensing (e.g., MetalMapper), or exploit multi-static array data acquisition (e.g., TEMTADS). They produce data of high density, quality, and diversity, and have been combined with advanced EMI models to provide superb classification performance relative to the previous generation of single-axis, monostatic sensors. However, these advances yet have to improve significantly our ability to classify small, deep, and otherwise challenging targets. Particularly, recent live-site discrimination studies at Camp Butner, NC and Camp Beale, CA have revealed that it is more challenging to detect and discriminate small munitions (with calibers ranging from 20 mm to 60 mm) than larger ones. In addition, a live-site test at the Massachusetts Military Reservation, MA highlighted the difficulties for current sensors to classify large, deep, and overlapping targets with high confidence. There are two main approaches to overcome these problems: 1) adapt advanced EMI models to the existing systems and 2) improve the detection limits of current sensors by modifying their hardware. In this paper we demonstrate a combined software/hardware approach that will provide extended detection range and spatial resolution to next-generation EMI systems; we analyze and invert EMI data to extract classification features for small and deep targets; and we propose a new system that features a large transmitter coil.

ESTCP live-site UXO classification results are presented for cued data collected by the Man Portable Vector (MPV) handheld sensor, at the Former Spencer Artillery Range in Tennessee. The site was contaminated with assorted munitions, ranging in caliber from 37 mm to 155 mm. The MPV data were collected in two areas: dynamic and wooded. The data sets are inverted using an advanced forward EMI model, the ortho-normalized volume magnetic source (ONVMS) model, combined with a direct-search optimization algorithm known as differential evolution. All data are inverted assuming one, two, and three sources. For each inversion, the targets’ intrinsic parameters are extracted and used in a library matching technique. Anomalies are classified as targets of interest (TOI) or clutter. Prioritized dig lists were constructed and submitted to the Institute for Defense Analysis for independent scoring. The result revealed an excellent classification performance by the advanced EMI models when applied to the Spencer Range MPV data. This paper describes the MPV sensor and the advanced models and demonstrates the Receiver Operating Characteristic curves for the cued MPV data collected at the Spencer Range.

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.

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.

ESTCP live-site UXO classification results are presented for cued data collected with two advanced EMI instruments, the cart-based 2 × 2 3D TEMTADS array and the Man Portable Vector (MPV) handheld sensor, at the former Camp Beale in California. There were two sets of targets of interest (TOI): the main set consisted of 105-mm, 81-mm, 60-mm, 37-mm and ISO projectiles, and the other (optional) set comprised site-specific fuzes and fuze fragments of varous sizes. The advanced models used for inversion and classification combine: 1) a joint-diagonalization (JD) algorithm that estimates the number of potential targets generating an anomaly directly from the measured data without need for inversion; 2) the ortho-normalized volume magnetic source (ONVMS) model, which locates targets, represents their EMI responses, and extracts their intrinsic feature vectors; and 3) a Gaussian mixture algorithm that uses extracted discrimination features to classify the corresponding buried objects as TOI or clutter. Initially the data are inverted using a combination of ONVMS and the differential evolution direct-search algorithm; this allows the determination of relevant intrinsic parameters, which in turn are classified by a mixture of clustering and library-matching techniques. This paper describes in more detail the main steps of the classification process and demonstrates the results obtained for the 2 × 2 3D TEMTADS and MPV data taken at Camp Beale, as scored independently by the Institute for Defense Analyses. The advanced models are seen to produce superb classification in both cases.

ESTCP live-site UXO classification results are presented for cued data collected with two advanced EMI instruments, the cart-based 2 × 2 3D TEMTADS array and the Man Portable Vector (MPV) handheld sensor, at the former Camp Beale in California. There were two sets of targets of interest (TOI): the main set consisted of 105-mm, 81-mm, 60-mm, 37-mm and ISO projectiles, and the other (optional) set comprised site-specific fuzes and fuze fragments of varous sizes. The advanced models used for inversion and classification combine: 1) a joint-diagonalization (JD) algorithm that estimates the number of potential targets generating an anomaly directly from the measured data without need for inversion; 2) the ortho-normalized volume magnetic source (ONVMS) model, which locates targets, represents their EMI responses, and extracts their intrinsic feature vectors; and 3) a Gaussian mixture algorithm that uses extracted discrimination features to classify the corresponding buried objects as TOI or clutter. Initially the data are inverted using a combination of ONVMS and the differential evolution direct-search algorithm; this allows the determination of relevant intrinsic parameters, which in turn are classified by a mixture of clustering and library-matching techniques. This paper describes in more detail the main steps of the classification process and demonstrates the results obtained for the 2 × 2 3D TEMTADS and MPV data taken at Camp Beale, as scored independently by the Institute for Defense Analyses. The advanced models are seen to produce superb classification in both cases.

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.

Classification tools including Support Vector Machines (SVM) and Neural Networks (NN) are employed, and their performances compared for Unexploded Ordnance (UXO) classification using live site electromagnetic induction (EMI) data. Both SVM and NN are examples of supervised machine-learning techniques, whose purpose is to label the features (extracted from the incoming data of the unknown subsurface anomalies) based on previously trained examples. In this paper a set of three features are extracted from the EMI decay curves of the physics-based intrinsic, effective dipole moment, called the total Normalized Surface Magnetic Source (NSMS). This data is first used to train both the SVM and NN models and further serves as a basis for UXO classification. These techniques are then compared to an unsupervised learning approach, based on agglomerative hierarchical clustering followed by Gaussian Mixture modeling. We found that such combination provides reduction in the amount of required training data (which is being requested solely based on the clustering results) and allows for convenient probabilistic interpretation of the classification. The classification results themselves depend on the UXO caliber, material composition and actual live UXO site's conditions. Therefore, here we report the classification results for a live UXO data set, collected at former Camp San Luis Obispo, CA. This study includes four targets-of-interest: 60-mm, 81-mm, and 4.2-in mortars and 2.36-in rockets. The classification performance between clutters and UXO is studied and the corresponding ROC curves are illustrated.

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.

Discrimination studies carried out on TEMTADS and Metal Mapper blind data sets collected at the San Luis Obispo UXO site are presented. The data sets included four types of targets of interest: 2.36" rockets, 60-mm mortar shells, 81-mm projectiles, and 4.2" mortar items. The total parameterized normalized magnetic source (NSMS) amplitudes were used to discriminate TOI from metallic clutter and among the different hazardous UXO. First, in object's frame coordinate, the total NSMS were determined for each TOI along three orthogonal axes from the training data provided by the Strategic Environmental Research and Development Program (SERDP) along with the referred blind data sets. Then the inverted total NSMS were used to extract the time-decay classification features. Once our inversion and classification algorithms were tested on the calibration data sets then we applied the same procedure to all blind data sets. The combined NSMS and differential evolution algorithm is utilized for determine the NSMS strengths for each cell. The obtained total NSMS time-decay curves were used to extract the discrimination features and perform classification using the training data as reference. In addition, for cross validation, the inverted locations and orientations from NSMS-DE algorithm were compared against the inverted data that obtained via the magnetic field, vector and scalar potentials (HAP) method and the combined dipole and Gauss-Newton approach technique. We examined the entire time decay history of the total NSMS case-by-case for classification purposes. Also, we use different multi-class statistical classification algorithms for separating the dangerous objects from non hazardous items. The inverted targets were ranked by target ID and submitted to SERDP for independent scoring. The independent scoring results are presented.

The Strategic Environmental Research and Development Program (SERDP) is administering benchmark blind tests of increasing realism to the UXO community. One of the latest took place at Aberdeen Proving Ground in Maryland: 214 cells, each one containing at most one buried target, were interrogated with the TEMTADS electromagnetic induction (EMI) sensor array. Each item could be one of six standard ordnance or could be harmless clutter such as shrapnel. The test called for singling out potentially dangerous items and classifying them. Our group divided the task into three steps: location, characterization, and classification. For the first step the HAP method was used. The method assumes a pure dipolar response from the target and finds the position and orientation using the measured field and its associated scalar potential, the latter computed using a layer of equivalent sources. For target characterization we used the NSMS model, which employs an ensemble of dipole sources arranged on a spheroidal surface. The strengths of these sources are normalized by the primary field that strikes them; their surface integral is an electromagnetic signature that can be used as a classifier. In this work we look into automating the classification step using a multi-category support vector machine (SVM). The algorithm runs binary SVMs for every combination of pairs of target candidates, apportions votes to the winners, and assigns unknown examples to the category with the most votes. We look for the feature combinations and SVM parameters that result in the most expedient and accurate classification.

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.

Discrimination between UXO and harmless objects is particularly difficult in highly contaminated sites where two or more objects are simultaneously present in the field of view of the sensor and produce overlapping signals. The first step in overcoming this problem is estimating the number of targets. In this work an orthonormalized volume magnetic source (ONVMS) approach is introduced for estimating the number of targets, along with their locations and orientations. The technique is based on the discrete dipole approximation, which distributes dipoles inside the computational volume. First, a set of orthogonal functions are constructed using fundamental solutions of the Helmholtz equations (i.e., Green's functions). Then, the scattered magnetic field is approximated as a series of these orthogonal functions. The magnitudes of the expansion coefficients are determined directly from the measurement data without solving an ill-posed inverse-scattering problem. The expansion coefficients are then used to determine the amplitudes of the responding volume magnetic dipoles. The algorithm's superior performance and applicability to live UXO sites are illustrated by applying it to the bi-static TEMTADS multi-target data sets collected by NRL personnel at the Aberdeen Proving Ground UXO teststand site.

The GEM-3D + sensor developed by Geophex, Ltd. is a new incarnation of their widely known GEM-3. The sensor provides the analyst with all three vector components of the secondary magnetic field over a wide range of frequencies. The GEM-3D + features an innovative "beacon-based" positioning system that provides a full description of its location and orientation at every point without requiring any on-sensor hardware beyond an electronic compass. This enhances the usefulness of the instrument for dynamic surveying, This paper presents some methods and results related to UXO identification using the GEM-3D + . Our analyses exploit data provided by the sensor in both grid-based and dynamic measurements to characterize different objects, including metal spheres and actual UXO. For the data analysis we alternate between the dipole model and the more rigorous standardized excitation approach. We review some ill-conditioning issues encountered with the latter model and the different approaches that we use to overcome them. In applications, the availability of horizontal field components in the data allow us to identify UXO vs. non-UXO items while minimizing the nonnegligible effects of ground response.

Recently, new generation, relatively sophisticated, ultra wideband EMI sensors with novel waveforms and multi-axis or vector receivers, have been developed which operate either in the time domain or in the frequency domain. Among these emerging technologies is the Time-domain Electromagnetic Multi-sensor Tower Array Detection System (TEMTADS). The system consists of 25 transmit/receive pairs arranged in a 5 × 5 grid, each with a square 35-cm diameter transmitter coil and a concentric square 25-cm receiver coil. The sensor activates the transmitter loops in sequence, and for each transmitter all receivers receive, measuring the complete transient response over a wide dynamic time range going approximately from 100 μs to 25 ms and distributed in 123 time gates. Thus it provides 625 data points at each location, without the need for a relative positioning system due to its fixed geometry. The combination of spatial diversity in the measurements and well-located sensor positions offers unprecedented data quality for discrimination processing algorithms. To take advantage of the data diversity that this instrument provides, we will use both of the following in an analysis of data acquired with the TEMTADS at Aberdeen Proving Ground (APG) in 2008: (1) advanced, physically complete EMI forward models such as the normalized surface magnetic source (NSMS) model and (2) a data-inversion scheme that uses the newly developed HAP method to estimate the location of a target. Initially the applicability of the NSMS and HAP algorithms to TEMTADS data sets are demonstrated by comparing the modeled data to test-stand and calibration data, and then the APG blind discrimination studies are conducted using as discrimination parameters the total NSMS and principal axes of the induced magnetic polarizability tensor for each target. The classification is done on the extracted feature vector via statistical classification tools.

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.

The underlying physics of low frequency EMI scattering phenomena in underwater environments from highly conducting and permeable metallic objects is analyzed using an approach that combines the method of auxiliary sources and a surface impedance boundary condition. The combined algorithm solves EMI boundary-value problems by representing the electromagnetic fields in each domain of the structure under investigation by a finite linear combination of analytical solutions of the relevant field equations, corresponding to elementary sources situated a small distance away from the boundaries of each domain. Numerical experiments are conducted for homogeneous and multilayer targets of canonical (spheroidal) shapes subject to frequency- or time-domain illumination, as well as for heterogeneous UXO like targets, to demonstrate: (a) how marine environments change EMI sensor performance and associated processing approaches for detecting highly conducting and permeable metallic objects underwater, and (b) what are the EMI sensors detectability limits. Near and far EMI field and induced eddy-current distributions are presented to help gain insight into underwater EMI scattering phenomena. Particularly, the results illustrate coupling effects between the object and its surrounding conductive medium, especially at high frequencies (early times for time-domain sensors). The results also suggest that this coupling depends on the object's material properties, the conductivity of the medium, and the distance between the sensor and the object's center.

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 Man Portable Vector (MPV) instrument is a time-domain handheld electromagnetic induction (EMI) instrument with five vector receivers and subcentimeter positioning accuracy. For cued interrogations, the MPV is designed to discriminate unexploded ordnance (UXO) from non-UXO using models ranging from the simple dipole model to physically complete models such as the Normalized Surface Magnetic Source (NSMS) method. The MPV acquires both EMI data and position at a 10Hz sampling rate resulting in 150 data points per second at each of a user selectable number time channels (typically 30-90) starting at 100 microseconds. Several factors might limit the usefulness of this data under real world conditions including an excess of usable data, noise in the position data, and insufficient coverage of anomalies. In this paper, we investigate the impact these factors have on the accuracy of discrimination results based on both static and dynamic MPV data. We investigate the effect of using only a subset of the data along with averaging techniques to reduce the amount of MPV data from a single anomaly. In addition, we inject various levels of noise into the position of the MPV in order to gauge the robustness of the discrimination results. Data is also selectively considered based on number of receivers and vector component(s). Results suggest that remarkably few data points are required for accurate discrimination results and that the vector receivers and low hardware noise of the MPV lead to robust results even with sparse data or noisy positional data.

Recently the SERDP/ESTCP office under the UXO Discrimination Pilot Study Program acquired high-density data over hundreds of targets using time-domain EM-63 sensor at Camp Sibert. The data were inverted and analyzed by various research groups using a simple dipole model approach and different classification tools. The studies demonstrated high discrimination probability with a low false-alarm rate. However in order to further improve discrimination between UXO and non-UXO items a better understanding is needed of the limits of current and emerging processing approaches. In this paper, the simple dipole model and a physically complete model called the normalized surface magnetic source (NSMS) the Camp Sibert data sets. The simple, infinitesimal dipole representation is by far the most widely employed model for UXO modeling. In this model, one approximates a target's response when excited by a primary (transmitted) field using an induced infinitesimal dipole (in turn described by a single magnetic polarizability matrix). The greatest advantage of the dipole model is that it is simple and imposes low computation costs. However, researchers have recently begun to realize the limitations of the simple dipole model as an inherently coarse description of the EMI behavior of complex, heterogeneous targets like UXO. To address these limitations, here the NSMS is employed as a more powerful forward model for data inversion and object discrimination. This method is extremely fast and equally applicable to the time or frequency domains. The object's location and orientation are estimated by using a standard nonlinear inversion-scattering approach. The discrimination performance between the dipole and NSMS models are conducted by investigating model fidelity and data density issues, positional accuracy and geological noise effects.

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.

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.

A multi dipole (MD) model is combined with a statistical algorithm called the mixed model to discriminate between objects of interest, such as unexploded ordnance (UXO), and innocuous items. In the multi dipole model (an extended version of the single dipole model), electromagnetic induction (EMI) responses for bodies of revolution (BOR) are approximated with a set of dipoles placed along the axis of symmetry of the objects. The model accurately takes into account the scatterer's heterogeneity along its axis of symmetry and is fast enough to invert digital geophysical data for discrimination purposes in real/near real time. Determining the amplitudes of the multi dipoles is an ill-posed problem that requires regularization. Obtaining the regularization parameters is not straightforward and in many cases is done via impractical supervised approaches. To overcome this problem, in this paper we combine a new statistical approach called the mixed model with the multi dipole model. Mixed modeling (MM) can be viewed as a generalization of the empirical Bayesian approach. It assumes that the forward model is not perfect: i.e., the model parameters (the amplitudes of the responding multi magnetic dipoles) contain random noise with zero mean and constant variance. Based on these assumptions, the method derives the regularization parameter from the variance of the least square error between the model and actual data using standard linear regression. Numerical results are presented to illustrate the theoretical basis and practical realization of the combined MD-mixed model (MD-MM) algorithm for UXO discrimination under real field conditions. In addition, a new condensed algorithm for determining the location and orientation of buried objects is introduced and tested against the ESTCP pilot discrimination study dynamic data set.

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 prohibitive costs of excavating all geophysical anomalies are well known and are one of the greatest impediments to efficient clean-up of unexploded ordnance (UXO)-contaminated lands at Department of Defense (DoD) and Department of Energy (DOE) sites. Innovative discrimination techniques that can reliably distinguish between hazardous UXO and non-hazardous metallic items are required. The key element to overcoming these difficulties lies in the development of advanced processing techniques that can treat complex data sets to maximize the probability of accurate classification and minimize the false alarm rate. To address these issues, this paper uses a new approach that combines a physically complete EMI forward model called the Generalized Standardized Excitation Approach (GSEA) with a statistical signal processing approach named Mixed Modeling (MM). UXO discrimination requires the inversion of digital geophysical data, which could be divided into two pars: 1) linear - estimating model parameters such as the amplitudes of the responding GSEA sources and 2) non-linear - inverting an object's location and orientation. Usually the data inversion is an ill-posed problem that requires regularization. Determining the regularization parameter is not straightforward, and in many cases depends on personal experience. To overcome this issue, in this paper we employ the statistical approach to estimate regularization parameters from actual data using the un-surprised mixed model approach. In addition, once the non-linear inverse scattering parameters are estimated then for UXO discrimination a covariance matrix and confidence interval are derived. The theoretical basis and practical realization of the combined GSEA-Mixed Model algorithm are demonstrated. Discrimination studies are done for ATC-UXO sets of time-domain EMI data collected at the ERDC UXO test stand site in Vicksburg, Mississippi.

The Man Portable Vector (MPV) sensor is a new mono/multistatic time-domain EMI detector that provides a detailed electromagnetic picture of a target by measuring all three magnetic field components at five distinct receiver positions in over 100 time channels. We have adapted the data-derived Standardized Excitation Approach (SEA) to this sensor. The SEA has been found in the past to make sound predictions in near-field situations, where schemes like the dipole model fail, and in cases where the target under interrogation is heterogeneous and the interactions between its different sections affect the detectable signal. The method replaces a given target with a set of sources placed on a surrounding spheroid and decomposes the sensor primary field into a set of standardized modes. Each of these modes elicits a response from the sources that is intrinsic to the object; it is only the relative weights of the modes that vary with the position and orientation of the target relative to the sensor. The strengths of the sources can be determined by fitting experimental data. Here we review some of the results we obtain when we apply the technique to problems relevant to the identification of unexploded ordnance (UXO). We extract the source parameters using high-quality measurements collected at a UXO test stand and invert unused data sets for location and to discriminate between different objects. We carry out similar experiments with buried objects in order to assess the performance of the method in realistic situations.

Studies have showed that magnetically susceptible soils significantly affect on the EMI sensors performances, which in return reduce the sensors discrimination capabilities. In order to improve EMI sensors detection and discrimination performances first soil's magnetic susceptibility needs to be estimated, and then the soils EMI responses have to be taken into account during geophysical data inversion procedure. Until now the soil's magnetic susceptibility is determined using a tiny amount (up to 15 mg) of soil's probe. This approach in many cases does not represent effective magnetic susceptibility that affects on the EMI sensors performances. This paper presents an approach for estimating soil's magnetic susceptibility from low frequency electromagnetic induction data and it is designed namely for the GeoPhex frequency domain GEM-3 sensor. In addition, a numerical code called the method auxiliary sources (MAS) is employed for establishing relation between magnetically susceptible soil's surface statistics and EMI scattered field. Using the MAS code EMI scatterings are studied for magnetically susceptible soils with two types of surfaces: body of revolution (BOR) and 3D rough surface. To demonstrate applicability of the technique first the magnetic susceptibility is inverted from frequency domain data that were collected at Cold Regions Research and Engineering Laboratory's test-stand site. Then, several numerical results are presented to demonstrate the relation between surface roughness statistic and EMI scattered fields.

In this paper the normalized surface magnetic charge model (NSMC) is employed for discriminating objects of interest, such as unexploded ordnances (UXO), from innocuous items, in cases when UXO electromagnetic induction (EMI) responses are contaminated by signals from other objects or magnetically susceptible ground. The model is designed for genuine discrimination and it is a physically complete, fast, and accurate forward model for analyzing EMI scattering. In the NSMC the overall EMI inverse problem can be summarized as follows: first, for any primary magnetic field the scattered magnetic field at selected points outside the object is recorded; and second, using the scattered field information an object buried object location, orientation and the amplitude of the NSMC are estimated. Finally, the total NSMC is used as a discriminant for distinguishing between UXO and non-UXO items. To illustrate the applicability of the NSMC algorithm, blind test data, which are collected at Cold Regions Research and Engineering Laboratory facility for actually buried objects under different type soil, are processed and analyzed.

This paper combines the normalized surface magnetic charge (NSMC) model and a pole series expansion method to determine the scattered field singularities directly from EMI measured data, i.e. to find a buried object location and orientation without solving a time consuming inverse-scattering problem. The NSMC is very simple to program and robust for predicting the EMI responses of various objects. The technique is applicable to any combination of magnetic or electromagnetic induction data for any arbitrary homogeneous or heterogeneous 3-D object or set of objects. In this proposed approach, first EMI responses are collected at a measurement surface. Then the NSMC approach, which distributes magnetic charge on a surface conformal, but does not coincide to the measurement surface, is used to extend the actual measured EMI magnetic field above the data collection surface for generating spatially distributed data. Then the pole series expansion approach is employed to localize the scattered fields singularities i.e. to determine the object's location and orientation. Once the object's location and orientations are found, then the total NSMC, which is characteristic of the object, is calculated and used for discriminating between UXO and non-UXO items. The algorithm is tested against actual EM-63 time domain EMI data collected at the ERDC test-stand site for actual UXO. Several numerical results are presented and discussed for demonstrating the applicability of the proposed method for determining buried objects location as well as for discriminating between objects on interested from non-hazardous items.

The identification of unexploded ordnance (UXO) using electromagnetic-induction (EMI) sensors involves two essentially independent steps: Each anomaly detected by the sensor has to be located fairly accurately, and its orientation determined, before one can try to find size/shape/composition properties that identify the object uniquely. The dependence on the latter parameters is linear, and can be solved for efficiently using for example the Normalized Surface Magnetic Charge model. The location and orientation, on the other hand, have a nonlinear effect on the measurable scattered field, making their determination much more time-consuming and thus hampering the ability to carry out discrimination in real time. In particular, it is difficult to resolve for depth when one has measurements taken at only one instrument elevation. In view of the difficulties posed by direct inversion, we propose using a Support Vector Machine (SVM) to infer the location and orientation of buried UXO. SVMs are a method of supervised machine learning: the user can train a computer program by feeding it features of representative examples, and the machine, in turn, can generalize this information by finding underlying patterns and using them to classify or regress unseen instances. In this work we train an SVM using measured-field information, for both synthetic and experimental data, and evaluate its ability to predict the location of different buried objects to reasonable accuracy. We explore various combinations of input data and learning parameters in search of an optimal predictive configuration.

Electromagnetic induction (EMI) has been shown to be a promising technique for unexploded ordnance (UXO) detection and discrimination. The excitation and response of a UXO or any other object to EMI sensors can be described in terms of scalar spheroidal modes consisting of associated Legendre functions. The spheroidal response coefficients Bjk correspond to the kth spheroidal response to the jth spheroidal excitation. The Bjk have been shown to be unique properties of an object, in that objects producing different scattered fields must be characterized by different Bjk. Therefore, the Bjk coefficients may be useful in discrimination. We use these coefficients rather than dipole moments because they are part of a physically complete, rigorous model of the object's response. Prolate spheroidal coordinate systems recommend themselves because they conform most readily to the proportions of objects of interest. In clearing terrain contaminated by UXO, the ability to distinguish larger buried metallic objects from smaller ones is essential. Here, a Support Vector Machine (SVM) is trained to sort objects into different size classes, based on the Bjk. The classified objects include homogeneous spheroids and composite metallic assemblages. Training a SVM requires many cases. Therefore, an analytical model is used to generate the necessary data. In simulation studies, the SVM is very successful in classifying independent sets of objects of the same type as the training set. Furthermore, we see that the Bjk are not related to size or signal strength of the object in any simple or visually discernible way. However, SVM is still able to sort the objects correctly. Ultimately, the success of the SVM trained with synthetic (model derived) data will be evaluated in application to data from a limited population of real objects, including UXO.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Electromagnetic Induction (EMI) is one of the most promising techniques for UXO discrimination. Target discrimination is usually formulated as an inverse problem typically requiring fast forward models for efficiency. The most successful and widely applied EMI forward model is the simple dipole model, which works well for simple objects when the observation points are not close to the target. For complicated cases, a single dipole is not sufficient and a number of dipoles (displaced dipoles) has been suggested. However, once more than one dipole is needed, it is difficult to infer a unique set of model parameters from measurement data, which is usually limited. Inspired by the displaced dipole model, we developed the dumbbell dipole model, which consists of a special combination of dipoles. We placed a center dipole and two anti-symmetric side dipoles on the target axis. The center dipole functions like the traditional single dipole model and the two side dipoles provide the non-symmetric response of the target. When the distance between dipoles is small, this model is essentially a dipole plus a quadrupole. The advantage of the dumbbell model is that the model parameters can be inferred more easily from measurement data. The center dipole represents the main response of the target, the side dipoles act as additional backup in case a simple dipole is not sufficient. Regularization terms are applied so that the dumbbell dipole model automatically reduces to the simple dipole model in degenerate cases. Preliminary test shows that the dumbbell model can fit the measurement data better than the simple dipole model, and the inferred model parameters are unique for a given UXO. This suggests that the model parameters can be used as a discriminator for UXO. In this paper the dumbbell dipole model is introduced and its performance is compared with that of both the simple dipole model and the displaced dipole model.

In the electromagnetic-induction (EMI) detection and discrimination of unexploded ordnance (UXO) it is important for inversion purposes to have an efficient forward model of the detector-target interaction. Here we revisit an attractively simple model for EMI response of a metallic object, namely a hypothetical anisotropic, infinitesimal magnetic dipole characterized by its magnetic polarizability tensor, and investigate the extent to which one can train a Support Vector Machine (SVM) to produce reliable gross characterization of objects based on the inferred tensor elements as discriminators. We obtain the frequency-dependent polarizability tensor elements for various object characteristics by using analytical solutions to the EMI equations. Then, using synthetic data and focusing on gross shape and especially size, we evaluate the classification success of different SVM formulations for different kinds of objects.