Yinlin Wang

Carbon fiber and void detection using high-frequency electromagnetic induction techniques

Ultrawide band electromagnetic induction (EMI) instruments have been traditionally used to detect high electric conductivity discrete targets such as metal unexploded ordnance. The frequencies used for this EMI regime have typically been less than 100 kHz. To detect intermediate conductivity objects like carbon fiber, even less conductive saturated salts, and even voids embedded in conducting soils, higher frequencies up to the low megahertz range are required in order to capture characteristic responses. To predict EMI phenomena at frequencies up to 15 MHz, we first modeled the response of intermediate conductivity targets using a rigorous, first-principles approach, the Method of Auxiliary Sources. A newly fabricated benchtop high-frequency electromagnetic induction instrument produced EMI data at frequencies up to that same high limit. Modeled and measured characteristic relaxation signatures compare favorably and indicate new sensing possibilities in a variety of scenarios.

Coil design considerations for a high-frequency electromagnetic induction sensing instrument

Intermediate electrical conductivity (IEC) materials (101S/m < σ < 104S/m), such as carbon fiber (CF), have recently been used to make smart bombs. In addition, homemade improvised explosive devices (IED) can be produced with low conducting materials (10-4S/m < σ < 1S/m), such as Ammonium Nitrate (AN). To collect unexploded ordnance (UXO) from military training ranges and thwart deadly IEDs, the US military has urgent need for technology capable of detection and identification of subsurface IEC objects. Recent analytical and numerical studies have showed that these targets exhibit characteristic quadrature response peaks at high induction frequencies (100kHz − 15MHz, the High Frequency Electromagnetic Induction (HFEMI) band), and they are not detectable with traditional ultra wideband (UWB) electromagnetic induction (EMI) metal detectors operating between 100Hz − 100kHz. Using the HFEMI band for induction sensing is not so simple as driving existing instruments at higher frequencies, though. At low frequency, EMI systems use more wire turns in transmit and receive coils to boost signal-to-noise ratios (SNR), but at higher frequencies, the transmitter current has non-uniform distribution along the coil length. These non-uniform currents change the spatial distribution of the primary magnetic field and disturb axial symmetry and thwart established approaches for inferring subsurface metallic object properties. This paper discusses engineering tradeoffs for sensing with a broader band of frequencies ever used for EMI sensing, with particular focus on coil geometries.

A Combined Joint Diagonalization-MUSIC Algorithm for Subsurface Targets Localization

This paper presents a combined joint diagonalization (JD) and multiple signal classification (MUSIC) algorithm for estimating subsurface objects locations from electromagnetic induction (EMI) sensor data, without solving ill-posed inverse-scattering problems. JD is a numerical technique that finds the common eigenvectors that diagonalize a set of multistatic response (MSR) matrices measured by a time-domain EMI sensor. Eigenvalues from targets of interest (TOI) can be then distinguished automatically from noise-related eigenvalues. Filtering is also carried out in JD to improve the signal-to-noise ratio (SNR) of the data. The MUSIC algorithm utilizes the orthogonality between the signal and noise subspaces in the MSR matrix, which can be separated with information provided by JD. An array of theoreticallycalculated Green’s functions are then projected onto the noise subspace, and the location of the target is estimated by the minimum of the projection owing to the orthogonality. This combined method is applied to data from the Time-Domain Electromagnetic Multisensor Towed Array Detection System (TEMTADS). Examples of TEMTADS test stand data and field data collected at Spencer Range, Tennessee are analyzed and presented. Results indicate that due to its noniterative mechanism, the method can be executed fast enough to provide real-time estimation of objects’ locations in the field.

Detecting and classifying small and deep targets using improved EMI hardware and data processing approach

The appearance of next-generation EMI sensors has been accompanied by the development of advanced EMI models and new interpretation and inversion schemes that take advantage of the richness and diversity of the data provided by these instruments. The technologies have been successfully tested in various scenarios, including ESTCP live-UXO classification studies, and have demonstrated superb classification performances. The studies have shown that the system’s ability to detect and classify small targets (i.e., calibers ranging from 20 to 60 mm) and deep targets (burial depth more than 11 times the target’s diameter) is still a challenging problem when an existing system is used. To overcome this problem, first the standard approach is analyzed, then targets detections are studied for different transmitter coil combinations and transmitter current magnitudes. The results are validated experimentally. The studies are done for a 37mm projectile placed at 42cm and 86 cm under the 2×2 TEMTADS instrument. The target detection and classification performances are illustrated for 6, 11 and 14 Ampere Tx currents using the joint diagonalization and ortho normalized volume magnetic source techniques.

AN EXPERT-FREE TECHNIQUE FOR LIVE SITE UXO TARGET CLASSIFICATION

In this paper we examine methods of automatic classification applied to Unexploded Ordnance (UXO) across data sets from a live site. All sensors used are time-domain Electromagnetic Induction (EMI) sensors. We solve for target extrinsic and intrinsic parameters using the Differential Evolution (DE) and Ortho-Normalized Volume Magnetic Source (ONVMS) algorithm. This inversion provides target locations and intrinsic time-series total ONVMS principal eigenvalues. We fit these to an empirical power decay model, the Pasion-Oldenburg model, providing dimensionality reduction for a Machine Learning (ML) approach. We group anomalies by the unsupervised Weighted-Pair Group Method with Averaging (WPGMA) algorithm. After requesting Ground Truths (GT) for the central element of each cluster, we train a supervised Gaussian Mixture Model (GMM), in which each class of UXO is represented by a multivariate Gaussian probability density. We request Ground Truths in rounds until we are confident there are no remaining Targets of Interest (TOI) in our survey of the site. Our system for UXO cleanup is fully automatic and expert free, and uses a priori knowledge combined with a learned algorithm.

Ultra-wide-band EMI sensing for subsurface deplete uranium detection and classification

Depleted uranium (DU) is a byproduct of the uranium enrichment process and contains less than 0.3 % of the radioactive U-235 isotope. Since, the natural uranium has about 0.72 % of the uranium U-235 isotope, the enrichment produces large quantities of low-level radioactive DU. The non-fissile uranium U-238 isotope constitutes the main component of DU and makes it very dense. With 19.1 g/cm3 density, the DU is about 68.4 % denser than lead. Because of its high density, the DU has been used for as armor-piercing penetrators by the U.S. army. There are at least 30 facilities where munitions containing DU have been evaluated or used for training. These evaluation studies have been conducted with and without catch-boxes and have left a legacy of DU contamination. Thus, there are needs for rapid and cost-effective approaches to detect and locate subsurface DU munitions and to assess large contaminated areas. In this paper, a new ultra-wideband (from 10s of Hertz up to 15 Megahertz) geophysical instrument is evaluated for sensing subsurface DU munitions and DU materials related to contaminations in soil. Namely, full electromagnetic induction (EMI) responses are investigated using computational and experimental data for a DU rod, dart, and three samples of Yuma Proving Ground (YPG) soils. Numerical data are obtained via the full 3D EMI solver based on the method of auxiliary sources. The EMI signals sensitivity with respect to DU size, orientations, and material composition are illustrated and analyzed. Comparisons between computational and experimental studies are demonstrated. The studies show that the new ultra-wideband EMI sensor measures the complete polarization relaxation response from the DU rod and dart, and is able to sense relative DU contamination levels in soil.

A high power EMI sensor for detecting and classifying small and deep targets

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.

Automatic classification of unexploded ordnance applied to live sites for MetalMapper sensor

This paper extends a previously-introduced method for automatic classification of Unexploded Ordnance (UXO) across several datasets from live sites. We used the MetalMapper sensor, from which extrinsic and intrinsic parameters are determined by the combined Differential Evolution (DE) and Ortho-Normalized Volume Magnetic Source (ONVMS) algorithms. The inversion provides spatial locations and intrinsic time-series total ONVMS principal eigenvalues. These are fit to a power-decay empirical model, providing dimensionality reduction to 3 coefficients (k, b, and g) for polarizability decay. Anomaly target features are grouped using the unsupervised clustering Weighted-Pair Group Method with Averaging (WPGMA) algorithm. Central elements of each cluster are dug, and the results are used to train the next round of dig requests. A Naive Bayes classifier is used as a supervised learning algorithm, in which the product of each features independent probability density represents each class of UXO in the feature space. We request ground truths for anomalies in rounds, until there are no more Targets of Interest (TOI) in consecutive requests. This fully automatic procedure requires no expert intervention, saving time and money. Naive Bayes outperformed previous efforts with Gaussian Mixture Models(GMM) in all cases.

Advanced EMI models for survey data processing: targets detection and classification

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.