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.
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
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.
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
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
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 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.
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.
Ever since their first experimental demonstration in 2000, the interest in metamaterials and negative refractive index materials has increased exponentially. This book covers the fundamental physical principles and emerging engineering applications of structured electromagnetic metamaterials that yield a negative refraction as well as other unexpected physical properties. It provides detailed explanations on the history, development, and main achievements of metamaterials.
This book discusses the design, optimization, and testing of structured metamaterials as well as their applications at frequencies ranging from radio wave to optical. It also explores novel concepts and phenomena, such as the perfect lens for super-resolution imaging, hyper lenses that couple the near-field to radiative modes, electromagnetic cloaking and invisibility, and near-field optical imaging.
Copublished by CRC Press
The correct form of electromagnetic wave momentum in matter has been debated in the literature for over
a century, with the main candidates being the Minkowski and the Abraham formulations. Recently, a third
momentum expression has been proposed, averaging the previous two, and has been argued to be the resolution
of the debate. In this paper, we revisit the various formulations and show that the debate has been carried
out on a wrong platform: the question is not which momentum expression is right and which is wrong, but
which is measurable and which is not. In that regard, experiments have been overwhelmingly in favor of the
Minkowski form, which, however, does not discredit the Abraham form as a theoretical concept. The third form
of momentum, averaging the previous two, is here argued to be merely the result of a mathematical exercise,
which physical assumptions need to be revisited.
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 B<sup>j</sup><sub>k</sub> correspond to the k<sup>th</sup> spheroidal response to the j<sup>th</sup> spheroidal excitation. The B<sup>j</sup><sub>k</sub> have been shown to be
unique properties of an object, in that objects producing different scattered fields must be characterized by different B<sup>j</sup><sub>k</sub>. Therefore, the B<sup>j</sup><sub>k</sub> 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 B<sup>j</sup><sub>k</sub>. 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 B<sup>j</sup><sub>k</sub> 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.