United States ERDC Cold Regions Research and Engineering Laboratory

NH, United States

United States ERDC Cold Regions Research and Engineering Laboratory

NH, United States
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Shamatava I.,Dartmouth College | Shamatava I.,Sky Research, Inc. | Shubitidze F.,Dartmouth College | Shubitidze F.,Sky Research, Inc. | And 6 more authors.
Proceedings of International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, DIPED | Year: 2012

Discrimination of challenging targets, such as small and deep targets in highly cluttered environments, is still an enormous problem for UXO industry. One way to improve target classification is to enhance the sensor hardware, while another way is to fully utilize the data provided by current EMI sensors, by deploying advanced signal processing approaches. To address the latter, our group developed a new ortho-normalized volume magnetic source (ONVMS) technique for representing subsurface targets responses, and for discriminating between UXO and non-UXO targets. The technique has been applied to live-site UXO data sets and demonstrated to be robust and tolerant to noise. In this paper the ONVMS is applied to the data from a small 20 mm projectile, acquired using an advanced, commercially available EMI system MetalMapper (MM). The target intrinsic parameters are first extracted from the data using both the ONVMS and simple dipole models, and then analyzed from library matching classification perspective. © 2012 Pidstryhach Institute of Applied Problem.


Shubitidze F.,Dartmouth College | Shubitidze F.,Sky Research, Inc. | Fernandez J.P.,Dartmouth College | Barrowes B.E.,United States ERDC Cold Regions Research and Engineering Laboratory | And 4 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

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. © 2010 Copyright SPIE - The International Society for Optical Engineering.


Shamatava I.,Sky Research, Inc. | Shamatava I.,Dartmouth College | Shubitidze F.,Sky Research, Inc. | Shubitidze F.,Dartmouth College | And 6 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011

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. © 2011 SPIE.


Shubitidze F.,Dartmouth College | Fernandez J.P.,Dartmouth College | Shamatava I.,Dartmouth College | Shamatava I.,Sky Research, Inc. | And 5 more authors.
Applied Computational Electromagnetics Society Journal | Year: 2010

The Normalized Surface Magnetic Source (NSMS) model is applied to unexploded ordnance (UXO) discrimination data collected at Camp Sibert, AL, with the EM63 electromagnetic induction sensor. The NSMS is a fast and accurate numerical forward model that represents an object's response using a set of equivalent magnetic dipoles distributed on a surrounding closed surface. As part of the discrimination process one must also determine the location and orientation of each buried target. This is achieved using a physics-based technique that assumes a target to be a dipole and extracts the location from the measured magnetic field vector and the scalar magnetic potential; the latter is reconstructed from field measurements by means of an auxiliary layer of magnetic charges. Once the object's location is estimated, the measured magnetic field is matched to NSMS predictions to determine the time- dependent amplitudes of the surface magnetic sources, which in turn can be used to generate classifying features. This paper shows the superior discrimination performance of the NSMS model. © 2010 ACES.


Shubitidze F.,Dartmouth College | Barrowes B.,United States ERDC Cold Regions Research and Engineering Laboratory | Shamatava I.,Sky Research, Inc. | Fernandez J.P.,Dartmouth College | O'Neill K.,United States ERDC Cold Regions Research and Engineering Laboratory
SEG Technical Program Expanded Abstracts | Year: 2011

The orttho-normalized volume magnetic source technique (ONMVS) [1] is applied to Camp Butner, NC, live-site UXO MetalMapper data inversion and subsurface metallic target discrimination. The ONVMS model can be considered as a generalized surface dipole model, and in fact reverts to the point dipole model as a limiting case. The method is based on the assumption that a collection of scatterers can be replaced with a set of magnetic dipole sources, distributed over a volume. These sources mimic the eddy currents and thereby the magnetic response that are induced on the targets by the primary magnetic field, and that in turn establish the observable secondary field. In this study, twenty-four hundred anomalies were processed. The anomaly sets included three types of UXOs: M48 Fuze, 105 mm and 37 mm projectiles. The effective total ONVMS amplitudes were used to discriminate UXO's from metallic clutter. The amplitudes of the total ONVMS were determined for each anomaly along three orthogonal axes by inverting MetalMapper data using the combined ONVMS and differential evolution algorithm. The inverted anomalies were ranked as UXO and non-UXO targets and submitted to the Institute for Defense Analyses (IDA) for independent scoring. The independent scoring results that are presented here, suggest that the ONVMS technique has the potential to improve UXO classification. © 2011 Society of Exploration Geophysicists.


Grzegorczyk T.M.,Delpsi LLC | Barrowes B.,United States ERDC Cold Regions Research and Engineering Laboratory | Shubitidze F.,Dartmouth College | Shubitidze F.,Sky Research, Inc. | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

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. © 2010 Copyright SPIE - The International Society for Optical Engineering.


Shubitidze F.,Dartmouth College | Shubitidze F.,Sky Research, Inc. | Pablo Fernandez J.,Dartmouth College | Shamatava I.,Dartmouth College | And 5 more authors.
Eurasip Journal on Advances in Signal Processing | Year: 2010

The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP) method to locate targets, the normalized surface magnetic source (NSMS) model to characterize them, and a support vector machine (SVM) to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%. Copyright © 2010 Juan Pablo Fernandez et al.


Shubitidze F.,Dartmouth College | Shubitidze F.,White River Technologies Inc. | Barrowes B.E.,Dartmouth College | Barrowes B.E.,United States ERDC Cold Regions Research and Engineering Laboratory | And 5 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

This paper describes procedures and approaches our team took to demonstrate the capability of advanced electromagnetic induction (EMI) forward and inverse models to perform subsurface metallic objects picking and classification at live-UXO sites from dynamic data sets. Over the past seven years, blind classification tests at live-UXO sites have revealed two main challenges: 1) consistent selection of targets for cued interrogation, (e.g., for the recent SWPG2 study, two independent performers that processed the same MetalMapper dynamic data picked different targets for cued interrogation); and 2) positioning of the cued sensor close enough to the actual cued target to accurately perform classification (particularly when multiple targets or magnetic soils are present). To overcome these problems, in this paper we introduced an innovative and robust approach for subsurface metallic targets picking and classification from dynamic data sets. This approach first inverts for target locations and polarizabilities from each dynamic data point, and then clusters the inverted locations and defines each cluster as a target/source. Finally, the method uses the extracted polarizabilities for classifying UXO from non-UXO items. The studies are done for the 2x2 TEMTADS dynamic data set collected at Camp Hale, CO. The targets picking and classification results are illustrated and validated against ground truth. © 2016 SPIE.


Shubitidze F.,Dartmouth College | Shubitidze F.,White River Technologies Inc. | Barrowes B.E.,Dartmouth College | Barrowes B.E.,United States ERDC Cold Regions Research and Engineering Laboratory | And 6 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

Detecting and classifying small (i.e., with calibers ranging from 20 to 60 mm) and deep targets (burial depth more than 11 times targets diameter) is still a challenging problem using current advanced EMI sensors and signal processing approaches. In order to overcome this problem, the standard time-domain NRL TEMTADS 2x2 electromagnetic induction (EMI) instrument is updated. Namely, the NRL TEMTADS 2x2 system's transmitter electronics is modified to increase transmitter (Tx) currents from 6 Amperes to 14 Amperes. The instrument has a Tx array with four coplanar square coils, together with four tri-axial receivers (Rx) placed at the center of each Tx. Each Rx cube contains three orthogonal coils and thus registers all three vector components of the impinging signals. The Tx coils, with transmitter currents of ∼14 A, illuminate a buried target, and the target responses are collected with a 500 kHz sample rate after turn off of the excitation pulse. The system operates in both static (cued) and dynamic modes. For cued mode, the raw decay measurements are grouped into 121 logarithmically-spaced "gates" whose center times range from 25 μs to 24.35 ms with 5% widths. The sensor is placed on a cart which provides a sensor-to-ground offset of 20 cm or less. In this paper, studies for APG Calibration, Blind, and Small Munitions Grids are presented and analyzed. The areas are arranged in grids of test cells and the cell center positions are known. Each target position is flagged with a non-metallic pin flag using cm-level GPS. The sensor is positioned over each target in turn. With the system positioned over the target, each Tx is activated sequentially and during off the Tx current, all four Rx record data. The capabilities of this sensor platform is rigorously investigated for UXO classification at APG blind and small munitions grids. © 2016 SPIE.


PubMed | United States ERDC Cold Regions Research and Engineering Laboratory
Type: Journal Article | Journal: The Journal of the Acoustical Society of America | Year: 2010

Experimental measurements were conducted using acoustic pulse sources in a full-scale artificial village to investigate the reverberation, scattering, and diffraction produced as acoustic waves interact with buildings. These measurements show that a simple acoustic source pulse is transformed into a complex signature when propagating through this environment, and that diffraction acts as a low-pass filter on the acoustic pulse. Sensors located in non-line-of-sight (NLOS) positions usually recorded lower positive pressure maxima than sensors in line-of-sight positions. Often, the first arrival on a NLOS sensor located around a corner was not the largest arrival, as later reflection arrivals that traveled longer distances without diffraction had higher amplitudes. The waveforms are of such complexity that human listeners have difficulty identifying replays of the signatures generated by a single pulse, and the usual methods of source location based on the direction of arrivals may fail in many cases. Theoretical calculations were performed using a two-dimensional finite difference time domain (FDTD) method and compared to the measurements. The predicted peak positive pressure agreed well with the measured amplitudes for all but two sensor locations directly behind buildings, where the omission of rooftop ray paths caused the discrepancy. The FDTD method also produced good agreement with many of the measured waveform characteristics.

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