J. R. McDonald

Multisensor towed array detection system for UXO detection

The multisensor towed array detection system (MTADS) was designed to be an efficient, sensitive tool for the detection and characterization of buried unexploded ordnance. It comprises arrays of total-field magnetometers and time-domain electromagnetic induction (EMI) sensors, associated navigation and data acquisition hardware, and a custom data analysis system. The MTADS has conducted eleven demonstrations and surveys. The system has shown the ability to detect ordnance at its likely self-penetration depths with a probability of detection of 0.95 or better The model-derived positions and depths of the detected ordnance items are generally well within the physical size of the targets, making remediation much quicker and less costly than with standard techniques. Data sets corresponding to many of the MTADS surveys are available to others in the field.

A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection

We focus on the development of signal processing algorithms that incorporate the underlying physics characteristic of the sensor and of the anticipated unexploded ordnance (UXO) target, in order to address the false alarm issue. In this paper, we describe several algorithms for discriminating targets from clutter that have been applied to data obtained with the multisensor towed array detection system (MTADS). This sensor suite includes both electromagnetic induction (EMI) and magnetometer sensors. We describe four signal processing techniques: a generalized likelihood ratio technique, a maximum likelihood estimation-based clustering algorithm, a probabilistic neural network, and a subtractive fuzzy clustering technique. These algorithms have been applied to the data measured by MTADS in a magnetically clean test pit and at a field demonstration. The results indicate that the application of advanced signal processing algorithms could provide up to a factor of two reduction in false alarm probability for the UXO detection problem.

Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys

The outputs from a physics-based modeler of magnetometry data have been successfully used with a probabilistic neural network (PNN) to discriminate unexploded ordnance (UXO) from ordnance-related scrap. Cross-validation predictions were performed on three data sets to determine which modeler parameters were most valuable for UXO classification. The best performing parameter combination consisted of the modeler outputs depth, size, and inclination. The cross-validation results also indicated that good prediction performance could be expected. Model outputs from one location at a site were used to train a PNN model, which could correctly discriminate UXO from scrap at a different location of the same site. In addition, data from one site, the former Buckley Field, Arapahoe County, CO, was used to predict targets detected at an entirely different training range. The Badlands Bombing Range, Bulls Eye 2 (BBR 2), Cuny Table, SD. Through careful selection of the probability threshold cutoff, the UXO detection rate obtained was 95% with a false alarm rate of only 37%. The ability to distinguish individual UXO types has been demonstrated with correct classifications between 71% and 95%.

UXO target detection using magnetometry and EM survey data

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.

Probabilistic neural networks for the discrimination of subsurface unexploded ordnance (UXO) in magnetometry surveys

The outputs from a physics-based modeler of magnetometry data have been successfully used with a probabilistic neural network (PNN) to discriminate UXO from scrap. Model outputs from one location at a site were used to train a PNN model, which could correctly discriminate UXO from scrap at a different location of the same site. Data from one site location, the Badlands Bombing Range, Bulls Eye 2 (BBR 2), was used to predict targets detected at a different location at the site, Badlands Bombing Range, Bulls Eye 1 (BBR 1) containing different types of items. The UXO detection rate obtained for this analysis was 93 percent with a false alarm rate of only 28 percent. The possibility of discriminant individual UXO types within the context of a coarser two- class problem was demonstrated. The utility of weighting the sum of squared errors in cross-validation optimization of the (sigma) parameter has been demonstrated as a method of improving the classification of UXO versus scrap.