B. E. Barrowes

Applying a volume dipole distribution model to next-generation sensor data for multi-object data inversion 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., Greens 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 algorithms 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.

Dumbbell dipole model and its application in UXO discrimination

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.

Camp Butner Live-Site UXO Classification Using Hierarchical Clustering and Gaussian Mixture Modeling

We demonstrate in detail a semisupervised scheme to classify unexploded ordnance (UXO) by using as an example the data collected with a time-domain electromagnetic towed array detection system during a live-site blind test conducted at the former Camp Butner in North Carolina, USA. The model that we use to characterize targets and generate discrimination features relies on a solution of the inverse UXO problem using the orthonormalized volume magnetic source model. Unlike other classification techniques, which often rely on library matching or expert knowledge, our combined clustering/Gaussian-mixture-model approach first uses the inherent properties of the data in feature space to build a custom training list that is then used to score all unknown targets by assigning them a likelihood of being UXO. The ground truth for the most likely candidates is then requested and used to correct the model parameters and reassign the scores. The process is repeated several times until the desired statistical margin is reached, at which point a final dig is produced. Our method could decrease intervention by human experts and, as the results of the blind test show, identify all targets of interest correctly while minimizing false-alarm counts.

MPV-II: an enhanced vector man-portable EMI sensor for UXO identification

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.

Pedemis: a portable electromagnetic induction sensor with integrated positioning

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.

Applying the physically complete EMI models to the ESTCP Camp Sibert Pilot Study EM-63 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 targets 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 objects 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.

Live-site UXO classification studies using advanced EMI and statistical models

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.

A combined NSMC and pole series expansion approach for UXO discrimination

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 objects location and orientation. Once the objects 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.

Combining electromagnetic induction and automated classification in a UXO discrimination blind test

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.

Man-portable vector EMI instrument data characterization using the NSMS method

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.