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.
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 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.
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.
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.
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.
This paper illustrates the discrimination performance of a set of advanced models at an actual UXO live site. The suite of
methods, which combines the orthonormalized volume magnetic source (ONVMS) model, a data-preprocessing
technique based on joint diagonalization (JD), and differential evolution (DE) minimization, among others, was tested at
the former Camp Beale in California. The data for the study were collected independently by two UXO production teams
from Parsons and CH2M HILL using the MetalMapper (MM) sensor in cued mode; each set of data was also processed
independently. Initially all data were inverted using a multi-target version of the combined ONVMS-DE algorithm,
which provided intrinsic parameters (the total ONVMS amplitudes) that were then used to perform classification after
having been inspected by an expert. Classification of the Parsons data was conducted by a Sky Research production team
using a fingerprinting approach; analysis of the CH2M HILL data was performed by a Sky/Dartmouth R&D team using
unsupervised clustering. During the classification stage the analysts requested the ground truth for selected anomalies
typical of the different clusters; this was then used to classify them using a probability function. This paper reviews the
data inversion, processing, and discrimination schemes involving the advanced EMI methods and presents the
classification results obtained for both the CH2M HILL and the Parsons data. Independent scoring by the Institute for
Defense Analyses reveals superb all-around classification performance.
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.
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.
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.
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.
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.
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.
KEYWORDS: Magnetism, Sensors, Data modeling, Electromagnetic coupling, Detection and tracking algorithms, Transmitters, Receivers, Target detection, Magnetic sensors, Signal detection
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 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.
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.
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 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.
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.
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.
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.
In this paper a physically complete model called the Normalized Surface Magnetic Source (NSMS) model is applied to data collected
using the Berkeley UXO Discriminator time-domain sensor. The sensor has three pairs of rectangular transmitters and eight pairs of
receivers that measure gradients of scattered fields. The system is cart-based and produces well-located EMI data sets. In order to take
advantage of this high quality data the NSMS technique is utilized for the BUD instrument. The NSMS is a very simple and robust
technique 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 3D object or set of objects. The NSMS approach uses
magnetic dipoles, distributed on a fictitious closed surface, as responding sources for predicting an object's EMI response. The
amplitudes of the NSMS sources are determined from actual measured data and the resulting total NSMS is used as a discriminant. To
demonstrate the applicability of the NSMS technique, we compare actual and predicted data for various UXO. The data were collected
at Yuma Proving Ground UXO sites by personnel from the University of California, Berkeley.
KEYWORDS: Data modeling, Electromagnetic coupling, Magnetism, Data acquisition, Receivers, Sensors, Signal to noise ratio, Electromagnetism, Optical spheres, Aluminum
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.
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.
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 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, 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.
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.
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.
Recently different time domain (TD) electromagnetic induction sensors have been developed and tested for UXO
detection and discrimination. These sensors produce well-located, multi-axis, high-density data. One of such sensors is
the Man Portable Vector (MPV) TD sensor build by G&G., Inc., which has two 75-cm diameter transmitter loops and
five tri-axial cubic receivers located around the transmitter coils. This sensor produces unprecedented high-fidelity
complete vector data sets. To take advantage of these high-quality data, in this paper we adapt the normalized surface
magnetic source (NSMS) model to the MPV. The NSMS is a very simple and robust technique 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. The NSMS approach uses magnetic
dipoles distributed on a fictitious closed surface as responding sources for predicting objects' EMI responses. The
amplitudes of the NSMS sources are determined from actual measured data, and at the end the total NSMS is used as a
discriminator. Usually, discrimination between UXO and non-UXO items is processed by first recovering the buried
object's location and orientation using standard non-linear minimization techniques; 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 developed physics-based approach, called (HAP), to estimate a buried object's
location and orientation. The approach assumes 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 sensor. 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.
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.
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.
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.
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.
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) 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.
KEYWORDS: Magnetism, Electromagnetic coupling, Sensors, Data modeling, Magnetic sensors, Received signal strength, Electromagnetism, Free space, Nose, Fourier transforms
The generalized standardized excitation approach (GSEA) is presented to enhance UXO discrimination under realistic field conditions. The GSEA is a fast, numerical, forward model for representing an object's EMI responses over the entire frequency band from near DC to 100s of kHz. It has been developed and tested in both the frequency and time domains for actual UXOs placed in free space. The GSEA, which uses magnetic dipoles instead of magnetic charges as responding sources, is capable of taking into account the background medium surrounding an object. Given a modeled UWB frequency domain (FD) response, the corresponding time domain (TD) response is easily obtained by the inverse Fourier transform. Thus the technique is applicable for any FD or TD sensor configuration and can treat complex data sets: novel waveforms, multi-axis, vector, or tensor magnetic or electromagnetic induction data, or any combination of magnetic and EMI data. Host media effects are taken into account via appropriate types of Green's function and equivalent dipole sources. Comparisons between simulations and experimental data illustrate that the GSEA is a unified approach for reproducing both TD and FD EMI signals for actual UXOs. The EMI response from a soil that has a frequency-dependent magnetic susceptibility is studied. The EMI responses in both FD and TD domains are analyzed for the model of an actual UXO that is buried in a magnetically susceptible half space.
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.
Magnetic and electromagnetic induction (EMI) sensing have been identified as two of most promising
technologies for the detection and discrimination of subsurface metallic objects, particularly unexploded ordnances
(UXO). In magnetic sensing, the principle of detection is that the sensor measures a distortion of the Earth's magnetic
field caused by ferrous objects/ordnance. Similarly, in EMI, the sensors are detecting signals that are produced by
induced and permanent magnetic polarizations. While these sensors can detect ferrous objects, they also find many other
magnetic anomalies in the close vicinity. Soils, which contain small magnetic particles, called magnetically susceptible
soils, can produce EMI responses, and therefore they can mask or modify the object's EMI response. These soils are a
major source of false positives when searching for UXO using magnetic or EMI sensors. Studies show that in adverse
areas up to 30% of identified electromagnetic (EM) anomalies are attributed to geology. Therefore, to enhance UXO
detection as well as discrimination in geological environments the effects of the magnetic soils on the magnetic and EMI
signal demands studies in detail. In this paper, the method of auxiliary sources (MAS) is applied to investigate the EMI
response from magnetically susceptible rough surfaces. Several important physical phenomena such as the interaction
between surface irregularities, modeled as multi hemitoroidal objects, surface roughness and antenna elevation effects
are studied and documented. The numerical results are checked against available measurement data.
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