An awareness of activities in operational environments is key to the U.S. Army’s strategy and many sensors and spectral regimes are employed to this end. Advances in wide-spectrum acoustic sensors and compact high performance computational hardware have created opportunities for enhancing awareness. The Engineer Research and Development Center (ERDC) is researching infrasound sensing as a means of persistent, remote monitoring to provide battlefield awareness. Machine learning techniques are used to identify unique signatures in the battlespace’s infrasonic environment. Given the limited number of labeled data sets, unsupervised Gaussian Mixture Modeling (GMM) is applied to identify these signatures utilizing the Short-term Fourier transform (STFT) and resulting Power Spectrum Density (PSD). This study describes the process of sorting collected infrasound data into categories based on PSDs for application to GMM algorithms that identify a characteristic class labeling. Labels in relatively short time frames are then associated with features seen throughout a 24 hour cycle to produce synthetic samples. Several Support Vector Machines are trained and used to separate in-class verses outlier features in time segments of new data. Outlier counts exceeding a threshold, typically 50%, label new data segments as novel and subject to further processing. Finally, efforts are d escribed f or directional focusing the array using multiple elements/sensors to localize signatures or to emphasize the signatures from different directions. GPU accelerations will be applied wherever possible to improve local bandwidth and throughput.
Intermediate electrical conductivity (IEC, 100-105 S/m) objects are increasingly important to properly detect and classify. For the US Military, carbon fiber (CF) "smart bomb" unexploded ordnance (UXO) are contaminating training ranges. Home-made explosives (HME) may also fit in this conductivity range. Objects in this conductivity range exhibit characteristic quadrature response peaks at high frequencies (100 kHz-15 MHz). Previous efforts towards electromagnetic induction (EMI) sensing of IEC targets have required single-turn, small-diameter transmitter (Tx) and receiver (Rx) loops. These smaller loops remain electrically short in the high frequency EMI (HFEMI) range (100 kHz-15 MHz), a necessary feature, but provide low signal-to-noise ratio (SNR), especially at low frequencies (1000 Hz-10 kHz). We propose a modification to our pre-production HFEMI instrument which has a hybrid low frequency/high frequency transmit coil. This hybrid system uses many turns in the traditional range, and a single wire turn at HFEMI frequencies, to maximize SNR across a wider EMI band. The turns which are not current carrying in high frequency mode must have negligible inductive coupling to the single current-carrying turn, low enough that any coupling is suitable for background subtraction. This is enforced by mutually disconnecting every turn from every other turn. The instrument uses the same calibration techniques as previously introduced,1 namely background subtraction and ferrite compensation. This paper dis- cusses engineering tradeoffs, compares results to numerical models and actual data from an advanced induction sensor, shows improvement in signal-to-noise ratio (SNR) at traditional EMI frequencies, and shows the same ability to detect IEC targets in the HFEMI band.
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.
Buried threats such as Improvised Explosive Devices (IEDs) and UneXploded Ordnance (UXO) can be composed of different materials including metal, carbon fiber, carbon rods, and nonconducting material such as wood, rubber, fuel oil, and plastic. Electromagnetic induction (EMI) instruments have been traditionally used to detect high electric conductivity discrete targets such as metal UXO. The frequencies used for this EMI regime have typically been less than 100 kHz. To detect intermediate conductivity objects like carbon fiber, higher frequencies up to the low megahertz range are required in order to capture characteristic relaxation responses. Nonconducting voids in an otherwise conducting background medium like soil channel currents around the void. These channeling currents exhibit relaxation responses similar to conducting targets but with a much higher frequency response. Nonconducting plastic landmines can be considered a void plus small metallic parts such as the firing pin, and a characteristic relaxation response due to both the void and the metal parts can be obtained which can reduce false alarms from EMI instruments that detect only the metal. To predict EMI phenomena at frequencies up to 15MHz, we modeled the response of conducting and nonconducting targets using 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.
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.
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.
This paper describes the relative polarization and reflectance characterization of background and selected target items to demonstrate the differences material type and source wavelength have on these measurements. The advanced reflectance and polarization instrument (ARPI) was modified to allow three lasers with different wavelengths to be used. This allowed for similar spot size, location, and angles to be used to collect the measurements. ARPI was used to collect polarized and cross-polarized returns from the polarized laser source at an incident angle of 0, 5, 10, and 20 degrees. These measurements were used to calculate the relative percent polarization and percent reflectance.
Analysis of the measured relative polarization and reflectance consists of single wavelength and multiwavelength comparisons with man-made and background items. A direct comparison is made between natural and man-made materials and different wavelengths of light. This careful comparison of differences between wavelengths will demonstrate which of the wavelengths produces the best and most consistent separation between background and manmade items. Our preliminary analysis shows that most man-made items give different polarization and reflectance returns than background items. Also, the analysis shows nominal variability between the three different wavelengths for background items and man-made items.
This paper describes the methodology for executing real-time simulations for the support of field testing of smart munition sensors. The sensor simulated is a dual-mode sensor using passive thermal infrared and active laser profiling. The types of tests supported by the simulation are dynamic flight tests over stationary targets, captive flight tests with moving tactical targets, and end-to-end system tests with dynamic flight over moving tactical targets. The components of this methodology that will be discussed include the sensor simulation model, target and background models, and measurement procedures for generating inputs required for target and background models. The resulting simulation capability can be used to support a wide range of evaluations including concept evaluation, subsystem design trade-off analysis, and system performance evaluation.
This paper analyzes the UXO classification capabilities of the GEM-3 using data collected for the Advanced UXO Detection/Discrimination Technology Demonstration at the U.S. Army Jefferson Proving Ground (JPG), Madison, Indiana. The approach taken in the US Army Engineer Research and Development Center (ERDC) analysis of the performance of the GEM-3 at JPG was to extract data points collected near each of the actual target locations and compare them to the calibration data acquired with known targets at the beginning of the demonstration. This was done to determine how well the data collected near each actual target matched the calibration signatures for the same ordnance type and the extent to which the data could be differentiated from other ordnance types and non-ordnance clutter. Classification of the targets was performed using a simple template-matching algorithm. This procedure resulted in an exact classification match for nearly half of the targets for which calibration data were available and a match to a similarly sized target for more than two-thirds of the medium and large targets. The sensor coverage of the test areas and the effect of test parameters such as ordnance size and depth on classification performance were also examined. New data were acquired with the GEM-3 to investigate the statistical variability of the instrument.
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