John B. Sigman

High frequency electromagnetic induction sensing for non-metallic ordnances detection

High frequency (>100 kHz) electromagnetic induction (HFEMI) sensing phenomena are investigated for nonmetallic ordnances detection and discrimination. HFEMI responses are studied using numerical and experimental data. The numerical modeling is done via the method of auxiliary sources, and data are collected using a new HEMI system, that has been developed at our lab. The comparisons between modeled and actual data are illustrated for a non-metallic 105 mm projectile.

Automatic classification of unexploded ordnance applied to Spencer Range live site for 5x5 TEMTADS sensor

This paper details methods for automatic classification of Unexploded Ordnance (UXO) as applied to sensor data from the Spencer Range live site. The Spencer Range is a former military weapons range in Spencer, Tennessee. Electromagnetic Induction (EMI) sensing is carried out using the 5x5 Time-domain Electromagnetic Multi-sensor Towed Array Detection System (5x5 TEMTADS), which has 25 receivers and 25 co-located transmitters. Every transmitter is activated sequentially, each followed by measuring the magnetic field in all 25 receivers, from 100 microseconds to 25 milliseconds. From these data target extrinsic and intrinsic parameters are extracted using the Differential Evolution (DE) algorithm and the Ortho-Normalized Volume Magnetic Source (ONVMS) algorithms, respectively. Namely, the inversion provides x, y, and z locations and a time series of the total ONVMS principal eigenvalues, which are intrinsic properties of the objects. The eigenvalues are fit to a power-decay empirical model, the Pasion-Oldenburg model, providing 3 coefficients (k, b, and g) for each object. The objects are grouped geometrically into variably-sized clusters, in the k-b-g space, using clustering algorithms. Clusters matching a priori characteristics are identified as Targets of Interest (TOI), and larger clusters are automatically subclustered. Ground Truths (GT) at the center of each class are requested, and probability density functions are created for clusters that have centroid TOI using a Gaussian Mixture Model (GMM). The probability functions are applied to all remaining anomalies. All objects of UXO probability higher than a chosen threshold are placed in a ranked dig list. This prioritized list is scored and the results are demonstrated and analyzed.

UXO classification procedures applied to advanced EMI sensors and models

Unexploded Ordnances (UXO) classification procedure consists of the following: background subtractions, data inversions and targets feature parameters estimations, and separating UXO from non-hazardous anomalies. First, each dataset is normalized by a corresponding Tx-current; then, all data files are background subtracted; third, the background corrected data are inverted and targets intrinsic (effective polarizabilities) and extrinsic (locations) are extracted; next, the extracted intrinsic and extrinsic parameters are used for generating prioritized and training targets lists; Finally, once the ground truth for training targets are provided, then prioritized targets are reclassified and final dig list is created. In this paper, the detailed steps of UXO classification procedure using the advanced EMI sensors and models are presented along with the processing and analysis approaches that are used to generate a prioritized dig list.

Detection of conductivity voids and landmines using high frequency electromagnetic induction

Electromagnetic induction (EMI) instruments have been traditionally used to detect high electric conductivity discrete targets such as metal unexploded ordnance (UXO). 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 relaxation responses. In this context, nonconducting lastic landmines can be considered a void plus small metallic parts such as the firing pin. To predict EMI phenomena at frequencies up to 15MHz, we modeled the response of conducting and nonconducting targets using the the Method of Auxiliary Sources. Our high-frequency electromagnetic induction (HFEMI) instrument is able to acquire 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 including the detection of voids and landmines.

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.

Accounting for the influence of salt water in the physics required for processing underwater UXO EMI signals

Processing electromagnetic induction signals from subsurface targets, for purposes of discrimination, requires accurate physical models. To date, successful approaches for on-land cases have entailed advanced modeling of responses by the targets themselves, with quite adequate treatment of instruments as well. Responses from the environment were typically slight and/or were treated very simply. When objects are immersed in saline solutions, however, more sophisticated modeling of the diffusive EMI physics in the environment is required. One needs to account for the response of the environment itself as well as the environment’s frequency and time-dependent effects on both primary and secondary fields, from sensors and targets, respectively. Here we explicate the requisite physics and identify its effects quantitatively via analytical, numerical, and experimental investigations. Results provide a path for addressing the quandaries posed by previous underwater measurements and indicate how the environmental physics may be included in more successful processing.