Classification, identification, and modeling of unexploded ordnance in realistic environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 205-218).Recovery of buried unexploded ordnance (UXO) is very slow and expensive due to the high false alarm rate created by clutter. Electromagnetic induction (EMI) has been shown to be a promising technique for UXO detection and discrimination. This thesis uses the EMI response of buried targets to identify or classify them. To perform such discrimination, accurate forward models of buried UXO are needed. This thesis provides a survey of existing target models: the dipole model, the spheroid model, and the fundamental mode model. Then the implementation of a new model, the spheroidal mode model, is described and validated against measurements of a UXO. Furthermore, an in-depth study of the effects of permeable soil, modeled as a permeable half space, is presented. This study concludes that the discontinuity created by the air to permeable soil interface produces minimal effect in the response of a buried object. The change is limited to a magnitude shift of the real portion of the EMI response and can be reproduced by superposition of a permeable half space response on the response of the same object in frees pace. Accurate soil modeling also allows one to invert for soil permeability values from measured data if such data are in known units. However, the EMI sensor used in this study provides measurements in consistent but unknown units. Furthermore, the instrument is from a third party and is proprietary. Therefore, this thesis describes the development of a non-invasive method to model and calibrate non-adaptive instruments so that all measurements can be converted into units consistent with modeled data. This conversion factor is shown to be a constant value across various conditions, thus demonstrating its validity.(cont.) Given that now a more complete model of the measurable response of a buried UXO is implemented, this study proceeds to demonstrate that EMI responses from UXO and clutter objects can be used to identify the objects through the application of Differential Evolution (DE), a type of Genetic Algorithm. DE is used to optimize the parameters of the UXO fundamental mode model to produce a match between the modeled response and the measured response of an unknown object. When this optimization procedure is applied across a library of models for possible UXO, the correct identity of the unknown object can be ascertained because the corresponding library member will produce the closest match. Furthermore, responses from clutter objects are shown to produce very poor matches to library objects, thus providing a method to discriminate UXO from clutter. These optimization experiments are conducted on measurements of UXO in air, UXO in air but obscured by clutter fragments, buried UXO, and buried UXO obscured by clutter fragments. It is shown that the optimization procedure is successful for shallow buried objects obscured by light clutter contributing to roughly 20 dB SNR, but is limited in applicability towards very deeply buried UXO or those in dense clutter environments. The DE algorithm implemented in this study is parallelized and the optimization results are computed with a multi-processor supercomputer. Thus, the computational requirement of DE is a considerable drawback, and the method cannot be used for real time, on-site inversion of measured UXO data. To address this concern, a different approach to inversion is also implemented in this study. Rather than identifying particular UXO, one may do a discrimination between general UXO and general clutter items. Previous work has shown that the expansion coefficients of EMI responses in the spheroidal coordinate system can uniquely characterize the corresponding targets.(cont.) Therefore, these coefficients readily lend themselves for use as features by which objects can be classified as likely to be UXO or unlikely to be UXO. To do such classification, the relationship between these coefficients and the physical properties of UXO and clutter, such as differences in size or body-of-revolution properties or material heterogeneity properties, must be found. This thesis shows that such relationships are complex and require the use of the automated pattern recognition capability of machine learning. Two machine learning algorithms, Support Vector Machines and Neural Networks, are used to identify whether objects are likely to be UXO. Furthermore, the effects of small diffuse clutter fragments and uncertainty about the target position are investigated. This discrimination procedure is applied on both synthetic data from models and measurements of UXO and clutter. It is found that good discrimination is possible for up to 20 dB SNR. But the discrimination is sensitive to inaccurate estimations of a target's depth. It is found that the accuracy must be within a 10 cm deviation of an object's true depth. The general conclusion forwarded by this work is that while increasingly accurate discrimination capabilities can be produced through more detailed forward modeling and application of robust optimization and learning algorithms, the presence of noise and clutter is still of great concern. Minimization or filtering of such noise is necessary before field deployable discrimination techniques can be realized.by Beijia Zhang.Ph.D

Classification of Metallic Targets Using a Walk-Through Metal Detection Portal

Metal detectors have been used for a long time for treasure hunting, security screening, and finding buried objects such as landmines or unexploded ordnance. Walk-through metal detection (WTMD) portals are used for making sure that forbidden or threatening metallic items, such as knives or guns, are not carried into secure areas at critical locations such as airports, court rooms, embassies, and prisons.The 9/11 terrorist act has given rise to stricter rules for aviation security worldwide, and the ensuing tighter security procedures have meant that passengers face more delays at airports. Moreover, the fear of terrorism has led to the adoption of security screening technology in a variety of places such as railway and coach stations, sports events, malls, and nightclubs.However, the current WTMD technology and scanning procedures at airports require that all metallic items be removed from clothing prior to scanning, causing inconvenience. Furthermore, alarms are triggered by innocuous items such as shoe shanks and artificial joints, along with overlooked items such as jewellery and belts. These lead to time- consuming, manual pat-down searches, which are found inconvenient, uncomfortable, and obtrusive by some.Modern WTMD portals are very sensitive devices that can detect items with only small amounts of metal, but they currently lack the ability to further classify the detected item. However, if a WTMD portal were able to classify objects reliably into, e.g., “knives”, “belts”, “keys”, the need for removing the items prior to screening would disappear, enabling a paradigm shift in the field of security screening.This thesis is based on novel research presented in five peer-reviewed publications. The scope of the problem has been narrowed down to a situation in which only one metallic item is carried through the portal at a time. However, the methods and results presented in this thesis can be generalized into a multi-object scenario. It has been shown that by using a WTMD portal and the magnetic polarisability tensor, it is possible to accurately distinguish between threatening and innocuous targets and to classify them into 10 to 13 arbitrary classes. Furthermore, a data library consisting of natural walk-throughs has been collected, and it has been demonstrated that the walk-through data collected with the above portal are subject to phenomena that might affect classification, in particular a bias and the so-called body effect. However, the publications show that, by using realistic walk-through data, high classification accuracy can be maintained regardless of the above problems. Furthermore, a self-diagnostics method for detecting unreliable samples has also been presented with potential to significantly increase classification accuracy and the reliability of decision making.The contributions presented in this thesis have a variety of implications in the field of WTMD-based security screening. The novel technology offers more information, such as an indication of the probable cause of the alarm, to support the conventional screening procedure. Moreover, eliminating the need for removing all metallic items prior to screening enables design of new products for scenarios such as sports events, where conventional screening procedures might be inconvenient, creating thus new business possibilities for WTMD manufacturing companies.The positive results give rise to a variety of future research topics such as using wideband data, enabling simultaneous classification of multiple objects, and developing the portal coil design to diminish signal nonlinearities. Furthermore, the ideas and the basic principles presented in this thesis may be applied to other metal detection applications, such as humanitarian demining