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A Procedure For Locating And Identifying Buried Unexploded Ordnance Using Curve Fitting Techniques And Neural Network Pattern Classification
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Book Synopsis A Procedure for Locating and Identifying Buried Unexploded Ordnance Using Curve Fitting Techniques and Neural Network Pattern Classification by : David Daniel Clark
Download or read book A Procedure for Locating and Identifying Buried Unexploded Ordnance Using Curve Fitting Techniques and Neural Network Pattern Classification written by David Daniel Clark and published by . This book was released on 2001 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multisensor Methods for Buried Unexploded Ordnance Detection, Discrimination, and Identification by :
Download or read book Multisensor Methods for Buried Unexploded Ordnance Detection, Discrimination, and Identification written by and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unexploded ordnance (UXO) cleanup is the number one priority Army installation remediation/restoration requirement The problem is enormous in scope, with millions of acres and hundreds of sites potentially contaminated. Before the UXO can be recovered and destroyed, it must be located. UXO location requires surface geophysical surveys. The geophysical anomalies caused by the UXO must be detected, discriminated from geophysical anomalies caused by other sources, and ideally identified or classified. Recent UXO technology demonstrations, live site demonstrations, and practical UXO surveys for site cleanup confirm that most UXO anomalies can be detected (with probabilities of detection of 90 percent or better), however there is little evidence of discrimination capability (i.e., the false alarm rates are high), and there is no identification capability. Approaches to simultaneously increase probability of detection and decrease false alarm rate and ultimately to give identification/classification capability involve rational multisensor data integration for discrimination and advanced development of new and emerging technology for enhanced discrimination and identification. The goal of multisensor data integration is to achieve true joint inversion of data to a best-fitting model using realistic physics-based models that replicate UXO geometries and physical properties of the UXO and surrounding geologic materials. Data management, analysis, and display procedures for multisensor data are investigated. The role of empirical, quasi-empirical, and analytical modeling for UXO geophysical signature prediction are reviewed and contrasted with approaches that require large signature databases (e.g., expert systems, neural nets, signature database comparison) for training or best-fit comparison. A magnetic modeling capability is developed, validated, and documented that uses a prolate spheroid model of UXO.
Book Synopsis Multisensor Methods for Buried Unexploded Ordnance Deteciton, Discrimination, and Identification by : Dwain Butler
Download or read book Multisensor Methods for Buried Unexploded Ordnance Deteciton, Discrimination, and Identification written by Dwain Butler and published by . This book was released on 1998 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unexploded ordnance (UXO) cleanup is the number one priority Army installation remediation restoration requirement. The problem is enormous in scope, with millions of acres and hundreds of sites potentially contaminated. Before the UXO can be recovered and destroyed, it must be located. UXO location requires surface geopbysical surveys. The geophysical anomalies caused by the UXO must be detected, discriminated from geophysical anomalies caused by other sources, and ideally identified or classified. Recent UXO technology demonstrations, live site demonstrations, and practical UXO surveys for site cleanup confirm that most UXO anomalies can be detected (with probabilities of detection of 90 percent or better), however there is little evidence of discrimination capability (i.e., the false alarm rates are high), and there is no identification capability. Approaches to simultaneously increase probability of detection and decrease false alarm rate and ultimately to give identification/classification capability involve rational multisensor data integration for discrimination and advanced development of new and emerging technology for enhanced discrimination and identification. The goal of multisensor data integration is to achieve true joint inversion of data to a best-fitting model using realistic physics-based models that replicate UXO geometries and physical properties of the UXO and surrounding geologic materials. Data management, analysis, and display procedures for multisensor data are investigated. A magnetic modeling capability is developed, validated, and documented that uses a prolate spheroid model of UXO. The electromagnetic modeling of UXO signatures is more problematic, and an intermediate quasi-empirical modeling capability (a simple analytical model modified to reflect measured signature observations) is explored.
Book Synopsis Classification, Identification, and Modeling of Unexploded Ordnance in Realistic Environments by : Beijia Zhang
Download or read book Classification, Identification, and Modeling of Unexploded Ordnance in Realistic Environments written by Beijia Zhang and published by . This book was released on 2008 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: (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.
Book Synopsis Using Artificial Neural Networks to Identify Unexploded Ordnance by : Jeffrey A. May
Download or read book Using Artificial Neural Networks to Identify Unexploded Ordnance written by Jeffrey A. May and published by . This book was released on 1997-06-01 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: The clearing of unexploded ordnance (UXO) is a deadly and time consuming process. The U.S. Government is currently spending millions of dollars to remove UXO's from bases that are closing around the world. Existing methods for detecting UXO's only inform the clearing team that a piece of metal is present, rather than the type of metal, either UXO, shrapnel, or garbage. A lot of time and money is spent digging up every piece of metal detected. This thesis presents the use of artificial neural networks to determine the type of UXO that is detected. A multi layered feed forward neural network using the back propagation training algorithm was developed using the language Lisp. The network was trained to recognize five pieces of ammunition. Results from the research show that four out of five pieces of ammunition from the test set were identified with an accuracy of .99 out of 1.0. The network also correctly identified that a tin can was not one of the five pieces of ammunition.
Book Synopsis Fast Algorithms for Subsurface Target Locating and Mapping in Unexploded Ordnance Detection by : Yinlin Wang
Download or read book Fast Algorithms for Subsurface Target Locating and Mapping in Unexploded Ordnance Detection written by Yinlin Wang and published by . This book was released on 2015 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unexploded ordnance (UXO) is a worldwide problem, which causes deaths and injuries to people living in post-conflict areas. It also prevents former military training sites from being returned to civilian use before they are properly cleaned. UXO cleaning is both dangerous and expensive. Metal detectors are the most common method used in UXO detection since reliable explosive detecting methods are not available at the current stage. Traditional metal detectors are unable to distinguish between targets of interest (TOI) and non-harzardous metallic clutter. As a result, UXO clean up could end up with about 95% of the digging to be clutter. To overcome this problem, new generation electromagnetic induction systems have recently been developed for subsurface target detection and classification. One of such system is the Time-Domain Electromagnetic Multisensor Towed Array Detection System (TEMTADS), which provides high fidelity EM data for target classification. UXO classification consists of three main steps: 1: Data collection; 2: Data inversion and target parameter extraction; 3: Target discrimination. In this thesis we present TEMTADS data sets inversion and processing approaches for cued (static) and dynamic (moving) measurements. Instead of using traditional matrix inversion and iterative search methods to tackle the highly non-linear problem of target locating, we employ the multiple signal classification (MUSIC) algorithm for fast and accurate estimation of target locations. The MUSIC algorithm is based on the orthogonality between the signal and noise subspaces in the multi static response (MSR) matrix of targets. In general, to identify the boundary between the two subspaces for actual data is a difficult task. To overcome this, joint-diagonalization (JD) is integrated into the processing. Namely, we use JD to estimate the number of sources presenting in a data set and to improve signal-to-noise ratio (SNR). The entire process is automated. Studies are done for test stand and blind data sets. Our results showed that the combined JD-MUSIC algorithm can be used to estimate target locations in near real time. The JD algorithm and Orthonormalized Volume Magnetic Source (ONVMS) model is extended for dynamic TEMTADS anomaly mapping and target picking. The algorithms were tested on data sets collected at live UXO sites in Camp Hale, Colorado. The study shows that JD and ONVMS provide underground target picking and preliminary classification capabilities, if clear background data can be provided. Comparison of inverted parameters between cued and dynamic data suggests that dynamic data can provide generally same value as cued data for classification purpose under good SNR conditions, thus the number of cued measurements needed can be reduced.
Book Synopsis Development of Data Fusion Algorithms for Detecting and Identifying Ordnance with Magnetometers and Ground Penetrating Radar by : Eugene R. Leach
Download or read book Development of Data Fusion Algorithms for Detecting and Identifying Ordnance with Magnetometers and Ground Penetrating Radar written by Eugene R. Leach and published by . This book was released on 1996 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report records work performed to develop data fusion algorithms to detect buried unexploded ordnance with magnetometers and ground penetrating radar. Task 1 characterized the performance of a cesium magnetometer, a gradiometer, and a 3-axis fiber optic magnetometer. Data which were measured and/or generated by validated models were used to develop a set of appropriate target features that could be used to identify and characterize buried ordnance. The applicability of applying techniques such as the use of neural nets, fuzzy logic and wavelets to the ordnance detection and identification problem were also evaluated.
Book Synopsis Locating and Characterizing Unexploded Ordnance Using Time Domain Electromagnetic Induction by :
Download or read book Locating and Characterizing Unexploded Ordnance Using Time Domain Electromagnetic Induction written by and published by . This book was released on 2001 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: A technique for interpreting Time Domain Electromagnetic (TEM) Data was proposed in "Detecting Unexploded Ordnance with Time Domain Electromagnetic Induction" (Pasion, 1999). An approximate forward modeling for the TEM response of compact metallic objects, an inversion for recovering model parameters, and relationships between model parameters and a target's physical parameters were established. These findings were combined to form an algorithm for locating and determining the approximate shape of a buried target
Book Synopsis International Aerospace Abstracts by :
Download or read book International Aerospace Abstracts written by and published by . This book was released on 1999 with total page 934 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Enhanced Signal Processing Algorithms for Buried Unexploded Ordnance Detection and Location Estimation with Magnetometer and Electromagnetic Induction Measurements by : Alan J. Witten
Download or read book Enhanced Signal Processing Algorithms for Buried Unexploded Ordnance Detection and Location Estimation with Magnetometer and Electromagnetic Induction Measurements written by Alan J. Witten and published by . This book was released on 1993 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Discrimination of Subsurface Unexploded Ordnance by : Kevin A. O'Neill
Download or read book Discrimination of Subsurface Unexploded Ordnance written by Kevin A. O'Neill and published by . This book was released on 2016 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unexploded ordnance (UXO) pose a persistent and expensive problem throughout the world; over 11 million acres are potentially contaminated in the U.S. alone. However, detection requires a very high degree of reliability, the false alarm rate is typically enormous, and cleanup costs are very high. This Tutorial Text addresses the unique challenges of UXO detection and the following topics: fundamental physics and phenomenology; new, successful modeling and analysis methods; the design, development, and testing of new instruments that provide expanded and superior data; innovative processing techniques; and highly successful discrimination performance in blind field tests at standardized sites. The book is written for lay scientists and engineers, as well as specialists in the field, requiring only some familiarity with basic vector calculus and matrix methods, common statistical concepts, and elementary physics.
Book Synopsis Unexploded Ordnance Detection and Mitigation by : James Byrnes
Download or read book Unexploded Ordnance Detection and Mitigation written by James Byrnes and published by Springer Science & Business Media. This book was released on 2008-12-19 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: The chapters in this volume were presented at the July–August 2008 NATO Advanced Study Institute on Unexploded Ordnance Detection and Mitigation. The conference was held at the beautiful Il Ciocco resort near Lucca, in the glorious Tuscany region of northern Italy. For the ninth time we gathered at this idyllic spot to explore and extend the reciprocity between mathematics and engineering. The dynamic interaction between world-renowned scientists from the usually disparate communities of pure mathematicians and applied scientists which occurred at our eight previous ASI’s continued at this meeting. The detection and neutralization of unexploded ordnance (UXO) has been of major concern for very many decades; at least since the First World war. UXO continues to be the subject of intensive research in many ?elds of science, incl- ing mathematics, signal processing (mainly radar and sonar) and chemistry. While today’s headlines emphasize the mayhem resulting from the placement of imp- vised explosive devices (IEDs), humanitarian landmine clearing continues to draw signi?cant global attention as well. In many countries of the world, landmines threaten the population and hinder reconstruction and fast, ef?cient utilization of large areas of the mined land in the aftermath of military con?icts.
Book Synopsis Detection of Buried Mines and Unexploded Ordnance (UXO). by :
Download or read book Detection of Buried Mines and Unexploded Ordnance (UXO). written by and published by . This book was released on 2007 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most military and commercial detectors sense the presence of metal casings or components of buried mines or explosive ordnance; however, this traditional approach to mine and unexploded ordnance (UXO) detection is prone to high false alarm rates. Explosive components, common to all mines and ordnance devices, offer a unique discriminator among buried objects. Two approaches to detect buried explosive devices directly are investigated: chemical trace detection, which relies on detecting either the vapor emanating from buried devices or the small explosive particles [and/or their explosive-related compounds (ERCs)] concentrated in the top soil, and radiation techniques, which uses radiation to probe beneath the earth's surface to provide bulk detection of buried explosive devices. This report describes the technology approaches and the current performance of each approach and discusses some promising new explosive detection technologies. It suggests that the Joint Unexploded Ordnance Coordination Office (JUXOCO) take a leadership role in establishing standards and protocols for reporting the results of field measurements in which trace analyses are used for detection of buried mines and ordnance and in sponsoring the development and maintenance of an open-sourced code to predict the variability of explosive detection in different environments.
Book Synopsis Sensor Feature Fusion for Detecting Buried Objects by :
Download or read book Sensor Feature Fusion for Detecting Buried Objects written by and published by . This book was released on 1993 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given multiple registered images of the earth's surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in a two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.
Book Synopsis Alternatives for Landmine Detection by : Jacqueline MacDonald Gibson
Download or read book Alternatives for Landmine Detection written by Jacqueline MacDonald Gibson and published by Rand Corporation. This book was released on 2003 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the rate that government and nongovernmental organizations are clearing existing landmines, it will take 450-500 years to rid the world of them. Concerned about the slow pace of demining, the Office of Science and Technology asked RAND to assess potential innovative technologies being explored and to project what funding would be required to foster the development of the more promising ones. The authors of this report suggest that the federal government undertake a research and development effort to develop a multisensor mine detection system over the next five to eight years.
Book Synopsis "Advances in Surface Penetrating Technologies for Imaging, Detection, and Classification.". by : Jay A. Marble
Download or read book "Advances in Surface Penetrating Technologies for Imaging, Detection, and Classification.". written by Jay A. Marble and published by . This book was released on 2007 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Landmine Monitor Report written by and published by Human Rights Watch. This book was released on 2004 with total page 1356 pages. Available in PDF, EPUB and Kindle. Book excerpt:
