Novel methods for detecting buried explosive devices

Oak Ridge National Laboratory (ORNL) and Quantum Magnetics, Inc. (QM) are exploring novel landmine detection technologies. Technologies considered here include bioreporter bacteria, swept acoustic resonance, nuclear quadrupole resonance (NQR), and semiotic data fusion. Bioreporter bacteria look promising for third-world humanitarian applications; they are inexpensive, and deployment does not require high-tech methods. Swept acoustic resonance may be a useful adjunct to magnetometers in humanitarian demining. For military demining, NQR is a promising method for detecting explosive substances; of 50,000 substances that have been tested, none has an NQR signature that can be mistaken for RDX or TNT. For both military and commercial demining, sensor fusion entails two daunting tasks, identifying fusible features in both present-day and emerging technologies, and devising a fusion algorithm that runs in real-time on cheap hardware. Preliminary research in these areas is encouraging. A bioreporter bacterium for TNT detection is under development. Investigation has just started in swept acoustic resonance as an approach to a cheap mine detector for humanitarian use. Real-time wavelet processing appears to be a key to extending NQR bomb detection into mine detection, including TNT-based mines. Recent discoveries in semiotics may be the breakthrough that will lead to a robust fused detection scheme

Plant transcriptional responses to explosives as revealed by \u3cem\u3eArabidopsis thaliana\u3c/em\u3e microarrays and its application in phytoremediation and phytosensing

This research focused on understanding genetic responses of plants to explosives, which is necessary to produce plants to detect and clean soil and water contaminated with toxic explosive compounds. The first study used microarray technology to reveal transcriptional changes in the model plant Arabidopsis thaliana exposed to the explosive compounds RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine; Royal Demolition Explosive or Research Department Explosive) and TNT (2,4,6-trinitrotoluene). This study yielded a list of genes up- and downregulated by explosive compounds, which can be potentially used for phytoremediation (remediation using plants) or phytosensing (detection using plants) of explosive compounds. The second study presented biotechnology tools to enhance phytosensing that might have application in not only explosives phytosensing but also sensing of other contaminants or important biological agents. This study addressed the problem of low detectable levels of reporter gene signal from a phytosensor and the results suggest the potential use of a site-specific recombination system to amplify the reporter gene signal. The final study addressed microarray data analysis and best practices for statistical analysis of microarray data. Standard parametric approaches for microarray analysis can be very conservative, indicating no unusable information from expensive microarray experiments. A nonparametric method of analysis on a variety of microarray datasets proved to be effective in providing reliable and useful information, when the standard parametric approach used was too conservative

Justification for Class III Permit Modification April 2000 ER Site 235 Storm Drain System Outfall Operable Unit 1309

This technical document contains the Statement of Basis for the Class III Permit Modification request for Site 235. Terminology changed to Justification for permit modifications after the submittal of this and the other documents included in this submittal. As with Justification Binders, contained within this document is the history of the site, descriptions of any investigations and/or remediation completed at the site, the regulatory background, and the rationale for the proposed future land-use of this site

Justification for Class III Permit Modification April 2000 ER Site 235 Storm Drain System Outfall Operable Unit 1309

This technical document contains the Justification for the Class III Permit Modification request for Site 235. Contained within this document is the history of the site, descriptions of any investigations and/or remediation completed at the site, the regulatory background, and the rationale for the proposed future land-use of this site

A CONCEPT VALIDATION OF A MAGNETOMETRY-BASED TECHNOLOGY FOR DETECTING CONCEALED WEAPONS IN VEHICLE DOOR PANELS

Acts of insurgency have become an increasing threat resulting in extensive measures being taken by the law enforcement authorities to mitigate their devastating effects on human life and infrastructure. This thesis introduces a magnetometry-based information, and signal processing methodology for detecting concealed ferrous objects in vehicle body panels. From extensive literature research, it was observed that while magnetic sensors have been used in a variety of related applications, but they have not been extensively applied to the on-road detection of firearms and explosives concealed in vehicles. This study utilized an extensive experimental protocol for preliminary concept validation. The main idea behind the approach was that almost all concealed weapons and explosives are made up of a considerable amount of ferrous material, and hence produce a local distortion in the Earth’s magnetic field. This distortion can then be identified by utilizing sensitive magnetic sensors. To detect concealed ferrous objects, magnetic signatures of a vehicle door panel were obtained by using a scanning assembly design in this thesis project, and compared to a base magnetic signature of the same vehicle door panel. The base magnetic signature is the magnetic field data of the same vehicle where no foreign ferrous objects were present. To analyze the data, a signal processing methodology was designed. To achieve the objective of accurately detecting concealed ferrous objects, simple measures such as magnetic field strength and its energy density were computed. These simple measures were then used in conjunction with more sophisticated statistical methods such as, normalized cross-correlation and Mahalanobis distance. Although all these methodologies were able to detect a magnetic footprint anomaly in the presence of a concealed object, the Mahalanobis distance approach, in particular provided the most conclusive results in all the test cases considered

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

Novel AI-assisted computational solutions for GPR data interpretation and electromagnetic data fusion to detect buried utilities

This research presents a number of novel computational solutions using artificial intelligence (AI) to interpret ground penetrating radar (GPR) data as well as fusing GPR data with data from other sensing modalities, including electromagnetic conductivity (EMC) and electromagnetic locating (EML). The application of the proposed computational solution is predominantly for detecting and locating buried utilities (e.g. pipes and cables) and ground anomalies (e.g. ground disturbances) in the shallow subsurface environment although the work can be extended to detect other buried anomalies. Processing GPR data is usually a subjective and time-consuming practise which involves expert intervention. Thus, the quality of the interpretation of such data depends on user experience and knowledge. Whilst several numerical approaches are available in the literature for post-processing GPR data, they all suffer from various shortcomings including lack of accuracy and/or excessive computational time. The issue is similar (or often worse) for data fusion between GPR and other sensors e.g. EMC and EML. To tackle some of these issues, in this research, four new computational procedures were developed. Three of these computational procedures are based on Kalman Filtering (KF), a less-studied approach to process GPR radargrams despite its great potential in efficient data analysis, and genetic algorithm (GA) as a machine learning based global optimisation tool. The final computational procedure combines finite element modelling and genetic algorithm to infer fused EML-GPR data. For the first two numerical methods, new algorithms were developed to optimise KF parameters using GA to remove noises from GPR radargrams and detect targets. The proposed procedures were validated against data from field and their performance was assessed against additional unseen dataset different to that of the validation to identify their potential limitations. Furthermore, their performances were compared against existing GPR data processing methods and differences were highlighted. The other two computational packages focused on data fusion from GPR and EMC/EML. The first of these two, extended the above KF algorithm to fuse data from GPR and EML as well as GPR and EMC. The results showed that the proposed data fusion algorithm significantly enhanced the quality of locating conductors and conductive regions in the subsurface compared to the individual techniques which were either incapable of defining the material of the buried target or the geometry of conductive anomalies. Finally, a novel inversion algorithm was developed by integrating finite element modelling of a coupled magnetic field and GA for detecting and locating buried live cables using GPR and EML. It was demonstrated that the proposed inversion can successfully detect the location of the buried cables as well as their intensity