Extremely low frequency measurements, below 30 Hz, of solid, thin-, and, thick-walled steel (permeable) cylinders with length-to-diameter ratios of approximately 4 are described and compared with the predicted response computed using a frequency domain finite element method (FDFEM). Measurements were made using a conventional EMI test setup consisting of a Hewlett Packard 89410 vector signal analyzer, rectangular transmitting and a figure-eight (bucked) receiving coil, along with appropriate transmitter and receiver coil amplifiers. All cylinders were measured with the predominant component of the excitatory magnetic field both aligned with and orthogonal to (two distinct measurements) the cylinder's axis. Measurements were made with and without a centered copper ring on the cylinders. The ring simulates the so-called rotating bands on actual UXO. Not surprisingly, we observed that the quadrature peak of the response shifts down in frequency much more when the axis of the ringed cylinder is aligned with the excitatory magnetic field than when perpendicular to it. Our measurements indicated that the real part of the response of the smallest cylinders measured asymptotically approaches its DC value around 1 Hz while the largest of the cylinders measured does not asymptote until well below 1 Hz. It appears that target information that may be crucial for discrimination purposes, especially for larger targets, exists at frequencies well below 30 Hz. Extremely low frequency measurements, especially with data averaging (stacking), can be a rather time consuming process, and therefore it is not likely that such measurements can be made from a moving platform. However, once an object of interest has been detected, the target can be reacquired and the measurement taken with the sensor stationary with respect to the target (sometimes referred to as a qued approach). As our measurements and simulations indicate, the qued method may be necessary if large solid UXO are to be distinguished from large thin-walled clutter objects.
We have designed and built a non-synchronous, sequential array of GEM-3 sensors for use with the Multi-sensor Towed Array Detection System (MTADS) with support from ESTCP. The roughly 2-m square array consists of three, 96-cm diameter GEM-3s in a triangular configuration. The GEM drive electronics have been modified to produce a substantially higher transmit moment, and thus increased sensitivity, than the standard GEM-3. The individual sensors transmit a composite waveform made up of ten frequencies from 30 Hz to 48 kHz for a single 1/30 s base period. Sequential operation allows two of these base periods for deconvolution and output of the frequency-dependent response from each GEM-3. After allowing for a short coil settling time between sensors, we achieve an array sampling rate of just over 9 Hz. Coupled with our standard survey speed of 3 mph, this results in a down-track sampling spacing of ~15 cm. The cross-track spacing is 50 cm. We have characterized these sensors at our Blossom Point test site. The static and dynamic response of the array to a variety of ordnance, ordnance simulants, and scrap is presented with consideration given to both detection and classification.
Digital filtering, principal component analysis (PCA), and an automated anomaly picker have been used to improve and automate target selection of unexploded ordnance (UXO). This is the first step in a three part program to develop new data analysis methods to automate target selection and improve discrimination of UXO from clutter and ordnance explosive waste (OEW) using magnetometry (Mag) and electromagnetic induction (EM) survey data. Traditionally, target detection has been accomplished by a time-consuming manual interactive data analysis approach. Experts screen the magnetometer data and select potential UXO targets based on their intuitive experience. EM data has been used in a secondary role in this process and the anomaly picking included classification and operator bias. In this program, the target detection step will use all of the data available and a separate classifier process will be used for identification and discrimination. Digital filtering is being used to enhance important features and reduce noise, while principal component analysis is being used to fuse three channels of data and reduce noise. Seven 50 meter-square data sets from two test sites were used to investigate these techniques. Features of interest are enhanced using filtering techniques. Inspection of the first- principal component suggests that data fusion of the magnetometer and EM data can be successfully accomplished. The new image consisting of circular features of varying diameters and intensities represent significant features present in all three data channels. Data with strong magnetometer and EM signals have the greatest intensity and in most cases noise is reduced. An automated anomaly picker has been designed to select targets from Mag, EM and PCA images. The method is fast and efficient as well as providing user options to control pick criteria.