This PDF file contains the front matter associated with SPIE Proceedings Volume 8017, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

Concealed Weapon Detection (CWD) has become a significant challenge to present day security needs; individuals carrying weapons into airplanes, schools, and secured establishments are threat to public security. Although controlled screening, of people for concealed weapons, has been employed in many establishments, procedures and equipment are designed to work in restricted environments like airport passport control, military checkpoints, hospitals, school and university entrance. Furthermore, screening systems do not effectively decipher between threat and non-threat metal objects, thus leading to high rate of false alarms which can become a liability to daily operational needs of establishments. Therefore, the design and development of a new CWD system to operate in a large open area environment with large numbers of people reduced incidences of false alarms and increased location accuracy is essential.

Detection and discrimination of unexploded ordnance (UXO) in areas of prior conflict is of high importance to the international community and the United States government. For humanitarian applications, sensors and processing methods need to be robust, reliable, and easy to train and implement using indigenous UXO removal personnel. This paper focuses on magnetometer sensing techniques, processing, and operation for UXO detection and discrimination applications. Specifically, we discuss the collection, processing, and discrimination of data collected using the PACMAG man-portable system consisting of arrays of sensitive total-field magnetometers, global positioning (GPS) combined with digital odometers, and a data acquisition system. We outline preliminary standard operating procedures for optimal collection of UXO-induced magnetic fields and associated position data using either a GPS, or odometer when surveying in GPS-denied areas. Processing techniques such as gridding and filtering, target picking, and discrimination lead to estimates of target size and location. Emphasis is placed on simplifying the production of magnetometer hardware and software for use by minimally-trained personnel with no advanced knowledge of magnetic sensing and geophysics.

The presence of unexploded ordnance (UXO), discarded military munitions (DMM), and munitions constituents (MC) at both active and formerly used defense sites (FUDS) has created a necessity for production-level efforts to remove these munitions and explosives of concern (MEC). Ordnance and explosives (OE) and UXO removal operations typically employ electromagnetic induction (EMI) or magnetometer surveys to identify potential MEC hazards in previously determined areas of interest. A major cost factor in these operations is the significant allocation of resources for the excavation of harmless objects associated with fragmentation, scrap, or geological clutter. Recent advances in classification and discrimination methodologies, as well as the development of sensor technologies that fully exploit physics-based analysis, have demonstrated promise for significantly reducing the false alarm rate due to MEC related clutter. This paper identifies some of the considerations for and the challenges associated with implementing these discrimination methodologies and advanced sensor technologies in production-level surveys. Specifically, we evaluate the implications of deploying an advanced multi-axis EMI sensor at a variety of MEC sites, the discrimination methodologies that leverage the data produced by this sensor, and the potential for productivity increase that could be realized by incorporating this advanced technology as part of production protocol.

Dynamic data from the MetalMapper electromagnetic induction sensor are analyzed using a fast inversion algorithm in order to obtain position information of buried anomalies. After validating the algorithm by comparing static and dynamic inversions from reference measurements at Camp San Luis Obispo, the algorithm is applied to realistic dynamic measurements from Camp Butner. A sequence of 939 data points are inverted as the MetalMapper travels along a calibration lane, flagging a few positions as corresponding to buried anomalies. An a posteriori comparison with field plots reveals a good agreement between the flagged positions and the field peak values, suggesting the efficacy of the algorithm at detecting a large variety of anomalies from dynamic data.

Classification tools including Support Vector Machines (SVM) and Neural Networks (NN) are employed, and their performances compared for Unexploded Ordnance (UXO) classification using live site electromagnetic induction (EMI) data. Both SVM and NN are examples of supervised machine-learning techniques, whose purpose is to label the features (extracted from the incoming data of the unknown subsurface anomalies) based on previously trained examples. In this paper a set of three features are extracted from the EMI decay curves of the physics-based intrinsic, effective dipole moment, called the total Normalized Surface Magnetic Source (NSMS). This data is first used to train both the SVM and NN models and further serves as a basis for UXO classification. These techniques are then compared to an unsupervised learning approach, based on agglomerative hierarchical clustering followed by Gaussian Mixture modeling. We found that such combination provides reduction in the amount of required training data (which is being requested solely based on the clustering results) and allows for convenient probabilistic interpretation of the classification. The classification results themselves depend on the UXO caliber, material composition and actual live UXO site's conditions. Therefore, here we report the classification results for a live UXO data set, collected at former Camp San Luis Obispo, CA. This study includes four targets-of-interest: 60-mm, 81-mm, and 4.2-in mortars and 2.36-in rockets. The classification performance between clutters and UXO is studied and the corresponding ROC curves are illustrated.

The Man-Portable Vector (MPV) electromagnetic induction sensor has proved its worth and flexibility as a tool for identification and discrimination of unexploded ordnance (UXO). TheMPV allows remediation work in treed and rough terrains where other instruments cannot be deployed; it can work in survey mode and in a static mode for close interrogation of anomalies. By measuring the three components of the secondary field at five different locations, the MPV provides diverse time-domain data of high quality. TheMPV is currently being upgraded, streamlined, and enhanced to make it more practical and serviceable. The new sensor, dubbedMPV-II, has a smaller head and lighter components for better portability. The original laser positioning system has been replaced with one that uses the transmitter coil as a beacon. The receivers have been placed in a configuration that permits experimental computation of field gradients. In this work, after introducing the new sensor, we present the results of several identification/discrimination experiments using data provided by the MPV-II and digested using a fast and accurate new implementation of the dipole model. The model performs a nonlinear search for the location of a responding target, at each step carrying out a simultaneous linear least-squares inversion for the principal polarizabilities at all time gates and for the orientation of the target. We find that the MPV-II can identify standard-issue UXO, even in cases where there are two targets in its field of view, and can discriminate them from clutter.

The challenges associated with removing UXO and explosive remnants of war have led to a variety of methods for detection and discrimination of buried metallic objects using time-domain electromagnetic induction (EMI). Recent work has shown that parameters recovered from physics-based inversions can discriminate and classify buried ordnance from non-ordnance. We present results of applying advanced processing to data from a dynamically repositioned multiaxis EMI instrument. Data are collected using an adaptive sampling process to find the center of the anomaly and collect minimal data while maintaining model fidelity. An ortho-normalized volume magnetic source (ONVMS) model is used to resolve various targets at different depths. The ONVMS model is a generalized volume dipole model, with the single dipole model being a special limiting case. Using the ONVMS model, an object's response to a sensor's primary magnetic field is modeled mathematically by a set of equivalent magnetic dipoles distributed inside a volume containing the object. We assess the utility and veracity of the dynamic sampling strategy coupled with the ONVMS model on data acquired over a set of calibration and simulant targets. Rapid target characterization codes are aggregated into a software package with particular focus on ease of use for non-expert users.

In this paper we present the inversion and classification performance of the advanced EMI inversion, processing and discrimination schemes developed by our group when applied to the ESTCP Live-Site UXO Discrimination Study carried out at the former Camp Butner in North Carolina. The advanced models combine: 1) the joint diagonalization (JD) algorithm to estimate the number of potential anomalies from the measured data without inversion, 2) the ortho-normalized volume magnetic source (ONVMS) to represent targets' EMI responses and extract their intrinsic "feature vectors," and 3) the Gaussian mixture algorithm to classify buried objects as targets of interest or not starting from the extracted discrimination features. The studies are conducted using cued datasets collected with the next-generation TEMTADS and MetalMapper (MM) sensor systems. For the cued TEMTADS datasets we first estimate the data quality and the number of targets contributing to each signal using the JD technique. Once we know the number of targets we proceed to invert the data using a standard non-linear optimization technique in order to determine intrinsic parameters such as the total ONVMS for each potential target. Finally we classify the targets using a library-matching technique. The MetalMapper data are all inverted as multi-target scenarios, and the resulting intrinsic parameters are grouped using an unsupervised Gaussian mixture approach. The potential targets of interest are a 37-mm projectile, an M48 fuze, and a 105-mm projectile. During the analysis we requested the ground truth for a few selected anomalies to assist in the classification task. Our results were scored independently by the Institute for Defense Analyses, who revealed that our advanced models produce superb classification when starting from either TEMTADS or MM cued datasets.

In this paper we employ advanced electromagnetic induction models to resolve multiple targets with overlapping EMI signals-i.e. to discriminate objects of interest, such as unexploded ordnance (UXO), from innocuous items. The models include a) a joint diagonalization (JD) technique that takes data from next-generation EMI sensors and uses the eigenvalues of the multistatic response matrix to estimate the number of potential targets, and b) the orthonormalized volume magnetic source (ONVMS) model, a physically complete, fast, and accurate forward model whose representation of a target's intrinsic EMI response is used to extract classification parameters. In the given approach the overall EMI inversion and classification problem proceeds as follows: first, the JD is applied to the data and the number of targets is estimated; once this is known, the ONVMS is combined with an optimization technique to yield the location and orientation of each buried object, as well as the amplitude of its ONVMS. Finally, a total ONVMS is calculated for each object and used as a discriminant to distinguish between UXO and non-UXO items and between different kinds of UXO. We illustrate the applicability of our multi-target analysis technique by using it on several teststand and live-site datasets collected with the TEMTADS sensor array. We end by demonstrating the superior performance of the ONVMS by applying it to multi-target blind-test data compiled at the Aberdeen Proving Ground test-stand facility.

Frequency-domain electromagnetic induction (EMI) sensors have the ability to provide target signatures which enable discrimination of landmines from harmless clutter. In particular, frequency-domain EMI sensors are well-suited for target characterization by inverting a physics-based signal model. In many model-based signal processing paradigms, the target signatures can be decomposed into a weighted sum of parameterized basis functions, where the basis functions are intrinsic to the target under consideration and the associated weights are a function of the target sensor orientation. The basis function parameters can then be used as features for classification of the target as landmine or clutter. In this work, frequency-domain EMI sensor data feature extraction and processing is investigated, with a variety of physics-based models and statistical classifiers considered. Results for data measured with a prototype frequency-domain EMI sensor at a standardized test site are presented. Preliminary results indicate that extracting physics-based features followed by statistical classification provides an effective approach for classifying targets as landmine or clutter.

Discrimination of buried exploded ordnance by inversion of electromagnetic data requires accurate sensor positioning. There are many contaminated areas were dense forest or significant topographic variation reduces accuracy or precludes use of standard geo-location methods, such as satellite-based Global Positioning System (GPS) and laser tracking systems (e.g., Robotic Total Station, RTS), as these rely on line of sight. We propose an alternative positioning system that is based on a beacon principle. The system was developed to survey with the Man-Portable Vector (MPV) EMI sensor. The magnetic moment of the MPV transmitter can be detected at a relatively large distance. The primary field is measured from a portable base station comprised of two vector receivers rigidly attached to either ends of a 1.5 meter horizontal boom. Control tests showed that relative location and orientation could be recovered with centimeter positional and one degree angular accuracy within a 3-4-meter range and 60-degree aperture (relative to boom transverse direction), which is more than sufficient to cover any UXO anomaly. This accuracy level satisfies commonly accepted positional requirement for discrimination. The beacon positioning system can facilitate classification of munitions in any man-trafficable area and was successfully deployed at a field demonstration.

In this study, a general structure for hand-held multi-sensor mine detection system is proposed. Ideal sensor configuration for multi-sensor mine detection system, requirements of hardware structures, data transfer issues and operational restrictions are discussed. The properties of a sample system designed according to the proposed structure are explained and a new graphical user interface is presented.

Synthetic aperture image reconstruction applied to outdoor acoustic recordings is presented. Acoustic imaging is an alternate method having several military relevant advantages such as being immune to RF jamming, superior spatial resolution, capable of standoff side and forward-looking scanning, and relatively low cost, weight and size when compared to 0.5 - 3 GHz ground penetrating radar technologies. Synthetic aperture acoustic imaging is similar to synthetic aperture radar, but more akin to synthetic aperture sonar technologies owing to the nature of longitudinal or compressive wave propagation in the surrounding acoustic medium. The system's transceiver is a quasi mono-static microphone and audio speaker pair mounted on a rail 5meters in length. Received data sampling rate is 80 kHz with a 2- 15 kHz Linear Frequency Modulated (LFM) chirp, with a pulse repetition frequency (PRF) of 10 Hz and an inter-pulse period (IPP) of 50 milliseconds. Targets are positioned within the acoustic scene at slant range of two to ten meters on grass, dirt or gravel surfaces, and with and without intervening metallic chain link fencing. Acoustic image reconstruction results in means for literal interpretation and quantifiable analyses. A rudimentary technique characterizes acoustic scatter at the ground surfaces. Targets within the acoustic scene are first digitally spotlighted and further processed, providing frequency and aspect angle dependent signature information.

The characteristics of unintended electromagnetic emissions from radio receivers are examined and we propose a novel method for detecting the presence of these emissions without a priori data or training. The chaotic properties of the internal oscillators from the radio receivers are modeled and used to detect the device emissions. A second-order self-similarity model is used to estimate the Hurst parameter as a detection threshold. The method is compared to a typical threshold method and is shown to be a significant improvement in the accuracy of detection. Upon detection, the received signal strength can be used to locate the device.

This effort is directed toward characterizing the response of a target-placed on or near the ground plane-to the radiation of a microwave source mounted on a moving vehicle. Because of the multipath problems inherent in measuring the electric field strength on or near the earth's surface, a theoretical model was constructed so that the effectiveness of the dynamic approach to actuating specific targets could be characterized. This process is repeated for a variety of microwave source speeds, vehicle-to-target-aspect ratios, and target positions on or above the ground plane. Given a horizontally polarized beam driven by a horn antenna, the electric field strengths were calculated at different vehicle-totarget- aspect ratios and at different heights on and above the ground plane for sand and clay soils. These values were then modeled in MATLAB to provide target stimulation for varied pulse chains. A model with independent pulse effects and a model based on the cumulative effect of pulses were evaluated.

Unattended ground monitoring that combines seismic and acoustic information can be a highly valuable tool in intelligence gathering; however there are several prerequisites for this approach to be viable. The first is high sensitivity as well as the ability to discriminate real threats from noise and other spurious signals. By combining ground sensing with acoustic and image monitoring this requirement may be achieved. Moreover, the DS Sentry^®provides innate spurious signal rejection by the "active-filtering" technique employed as well as embedding some basic statistical analysis. Another primary requirement is spatial and temporal coverage. The ideal is uninterrupted, long-term monitoring of an area. Therefore, sensors should be densely deployed and consume very little power. Furthermore, sensors must be inexpensive and easily deployed to allow dense placements in critical areas. The ADVIS DS Sentry^®, which is a fully-custom integrated circuit that enables smart, micro-power monitoring of dynamic signals, is the foundation of the proposed system. The core premise behind this technology is the use of an ultra-low power front-end for active monitoring of dynamic signals in conjunction with a highresolution, Σ Δ-based analog-to-digital converter, which utilizes a novel noise rejection technique and is only employed when a potential threat has been detected. The DS Sentry® can be integrated with seismic accelerometers and microphones and user-programmed to continuously monitor for signals with specific signatures such as impacts, footsteps, excavation noise, vehicle-induced ground vibrations, or speech, while consuming only microwatts of power. This will enable up to several years of continuous monitoring on a single small battery while concurrently mitigating false threats.

A new technique for improvised explosive device (IED) creation uses an explosive device buried in foam and covered in a layer of dirt. These devices are difficult to detect visually, however, their material characteristics make them detectable by passive millimeter-wave (pmmW) sensors. Results are presented from a test using a mock IED and an outdoor set-up consisting of two mock IEDs on a dirt background. The results show that the mock IEDs produces a millimeter-wave signature which is distinguishable from the background surrounding the mock IEDs. Simulations based on the measured data are presented and a design for a future vehicle mounted sensor is shown.

This report describes the results of the first phase of a planned two-phase program to develop laser technology for rapid neutralization of buried munitions from a safe standoff distance. The primary objective of this first phase is to demonstrate, via laboratory experiments, the capabilities of a breadboard laser system to "drill" through a minimum depth of 15 cm of earthen materials to defeat a buried mine at a standoff distance greater than 20 m. In the initial phase covered by this report, results of short range laboratory testing by 3 contractors are reported. The planned second phase of this program will consist of procuring a more capable 10 kW SM laser and performing field-testing at longer standoff ranges.

The purpose of this paper is to define the vision and future strategy for advancing the use of automation in underwater mine recognition. The technical portion of this strategy is founded on the principle of adapting the automation in situ based on a highly variable environment / context and the occasional availability of the human operator. To frame this strategy, a survey of past and current algorithm development for underwater mine recognition is presented and includes a detailed description on adaptive algorithms. This discussion is motivated by illustrating the extreme variability in the underwater environment and that performance estimation techniques are now emerging that are capable of quantifying these variations in situ. It is the in situ linkage of performance estimation with adaptive recognition that forms one of the key technological enablers of this future strategy. The non-technical portion of this strategy is centered on enabling an effective human-machine team. Enabling this teaming relationship involves both gaining trust and establishing an overall support system that is amenable to such human-machine interactions. Aspects of trust include both individual trust and institutional trust, and a path for gaining both is discussed. Overall aspects of the support system are highlighted and include standards for data and interoperability, network-centric software architectures, and issues in proliferating knowledge that is learned in situ by multiple distributed algorithms. This paper concludes with an articulation of several important and timely research questions concerning automation for underwater mine recognition.

A synthetic aperture sonar (SAS) image segmentation algorithm using features from a parameterized intensity image autocorrelation function (ACF) is presented. A modification over previous parameterized ACF models that better characterizes periodic or rippled seabed textures is presented and discussed. An unsupervised multiclass k-means segmentation algorithm is proposed and tested against a set of labeled SAS images. Segmentation results using the various models are compared against sand, rock, and rippled seabed environments.

Frame methods, basis expansion methods, or kernel methods provide a higher-dimensional representation of a given dataset within a feature space for discrimination applications. Frame pursuit addresses the problem of searching for optimal frames to improve classification for pattern recognition applications. In this paper, the results of two stochastic optimization techniques applied to the optimal frame problem are presented. The cost function is a k-nearest-neighbor function. These techniques are tested here over six datasets. Empirical results demonstrate the utility of frame transformations for improving performance results in pattern recognition applications.

In this paper we present a method for clustering and classification of acoustic color data based on statistical analysis of functions using square-root velocity functions (SVRF). The convenience of the SVRF is that it transforms the Fisher-Rao metric into the standard L² metric. As a result, a formal distance can be calculated using geodesic paths. Moreover, this method allows optimal deformations between acoustic color data to be computed for any two targets allowing for robustness to measurement error. Using the SVRF formulation statistical models can then be constructed using principal component analysis to model the functional variation of acoustic color data. Empirical results demonstrate the utility of functional data analysis for improving performance results in pattern recognition using acoustic color data.

Automatic Change Detection (ACD) compares new and stored terrain images for alerting to changes occurring over time. These techniques, long used in airborne radar, are just beginning to be applied to sidescan sonar. Under the right conditions ACD by image correlation-comparing multi-temporal image data at the pixel or parcel level-can be used to detect new objects on the seafloor. Synthetic aperture sonars (SAS)-coherent sensors that produce fine-scale, range-independent resolution seafloor images-are well suited for this approach; however, dynamic seabed environments can introduce "clutter" to the process. This paper explores an ACD method that uses salience mapping in a global-to-local analysis architecture. In this method, termed Temporally Invariant Saliency (TIS), variance ratios of median-filtered repeat-pass images are used to detect new objects, while deemphasizing modest environmental or radiometric-induced changes in the background. Successful tests with repeat-pass data from two SAS systems mounted on autonomous undersea vehicles (AUV) demonstrate the feasibility of the technique.

Assuming that a 2D surface is a representation of a manifold embedded in 3-space then metrics of the eigenfunctions of the diffusion maps of that manifold represent the shape of that manifold with invariance to rotation, scale, and translation. Diffusion maps is said to preserve the local proximity between data points by constructing a representation for the underlying manifold by an approximation of the Laplace-Beltrami operator acting on the graph of this surface. This work examines 3D shape clustering problems using metrics of the projections onto the natural and nodal sets of the Laplace-Betrami eigenfunctions for shape analysis of closed surfaces. Results demonstrate that the metrics allow for good class separation over multiple targets with noise.

In this work, we propose DEformable BAyesian Networks (DEBAN), a probabilistic graphical model framework where model selection and statistical inference can be viewed as two key ingredients in the same iterative process. While this concept has shown successful results in computer vision community,^1-4 our proposed approach generalizes the concept such that it is applicable to any data type. Our goal is to infer the optimal structure/model to fit the given observations. The optimal structure conveys an automatic way to find not only the number of clusters in the data set, but also the multiscale graph structure illustrating the dependence relationship among the variables in the network. Finally, the marginal posterior distribution at each root node is regarded as the fused information of its corresponding observations, and the most probable state can be found from the maximum a posteriori (MAP) solution with the uncertainty of the estimate in the form of a probability distribution which is desired for a variety of applications.

Our previous work developed an online learning Bayesian framework (dynamic tree) for data organization and clustering. To continuously adapt the system during operation, we concurrently seek to perform outlier detection to prevent them from incorrectly modifying the system. We propose a new Bayesian surprise metric to differentiate outliers from the training data and thus help to selectively adapt the model parameters. The metric is calculated based on the difference between the prior and the posterior distributions on the model when a new sample is introduced. A good training datum would sufficiently but not excessively change the model; consequently, the difference between the prior and the posterior distributions would be reasonable to the amount of new information present on the datum. However, an outlier carries an element of surprise that would significantly change the model. In such a case, the posterior distribution would greatly differ from the prior resulting in a large value for the surprise metric. We categorize this datum as an outlier and other means (e.g. human operator) will have to be used to handle such cases. The surprise metric is calculated based on the model distribution, and as such, it adapts with the model. The surprise factor is dependent on the state of the system. This speeds up the learning process by considering only the relevant new data. Both the model parameters and even the structure of the dynamic tree can be updated under this approach.

Because the recovery of underwater munitions is many times more expensive than recovering the same items on dry land, there is a continuing need to advance marine geophysical characterization methods. To efficiently and reliably conduct surveying in marine environments, low-noise geophysical sensors are being configured to operate close to the sea bottom. We describe systems that are deployed from surface vessels via rigid or flexible tow cables or mounted directly to submersible platforms such as unmanned underwater vehicles. Development and testing of a towed configuration has led to a 4 meter wide hydrodynamically stable tow wing with an instrumented top-side assembly mounted on the stern of a surface survey vessel. An integrated positioning system combined with an instrumented cable management system, vessel and wing attitude and wing depth measurements to provide sub-meter positional accuracy in up to 25 meter water depths and within 1 to 2 meters of the seafloor. We present the results of data collected during an instrument validation survey over a series of targets emplaced at measured locations. Performance of the system was validated through analyses of data collected at varying speeds, headings, and height above the seafloor. Implementation of the system during live-site operations has demonstrated its capability to survey hundreds of acres of marine or lacustrine environment. Unique deployment concepts that utilize new miniaturized and very low noise sensors show promise for expanding the applicability of magnetic sensing at marine sites.

The detection of munitions targets obscured in coastal and marine settings has motivated the need for advanced geophysical technologies suited for underwater deployment. Building on conventional marine electromagnetic theory and based on the use of existing electric and magnetic field sensing designs, we analyze the electromagnetic fields emitted from excited targets in the frequency range between 1 kHz and 1 MHz. We present evidence that employing electromagnetic modes that are higher in frequency relative to those typically used in ground-based sensing yields greater range and sensitivity for underwater surveys. We develop potential design strategies for implementing both magnetic (B) and electric (E) field sources and sensors in the marine environment, and determine optimal arrangements for a potential combined E- and B-field sensing system. The implementation of both 1D analytical and 3D numerical simulations yields the primary and secondary field distributions in representative underwater settings for various sourcereceiver arrangements. We study the electromagnetic field distributions from both electric (voltage-fed dipole) and magnetic field (encased and submerged induction coil) active sources. Application of these concepts provide unique and useful information about targets from the addition of electric field sensing alone as well as through the combination of electric and magnetic field sensing.

In this work, new configurations of magnetic field transmitter coils (Tx) and receiver sensors (Rx) are studied for underwater (UW) geo-locations. The geo-location system, based on low frequency magnetic fields, uses measured vector magnetic fields at a given set of points in space. It contains an active pulsed direct current transmitter, tri-axial field receivers, and a global positioning system unit (GPS). The GPS is coupled with the EMI system and provides continuous geo-referencing of the UW system's position. UW geolocations are estimated using a) closed form solution, that uses the total vector magnetic field tensor's gradient, and b) nonlinear optimization technique based of the differential evolution (DE) algorithm. In this work we first investigated the advantages and disadvantages of the proposed UW low frequency magnetic field geo-location system. Namely, we present systematic studies on: a) magnetic field transmitter configurations to determine the best compromise between size, shape and practical implementation to achieve maximum transmitter range in the UW environment, b) the placements of tri-axial receiver sensors with respect to the Tx to accurately estimate the UW geo-location from the measured magnetic fields; c) different sources of noise (such as the air-water interface, coupling between targets' EMI responses and the geo-location system's signals, water conductivity), to estimate how these noises influence the system's performance and localization precision. Finally, we assessed the capabilities of the closed-form solution and the DE technique to predict the location of an underwater interrogation system by comparing their corresponding estimated results to the true value. We found that for realistic water conductivities, the frequency should be less than 100 Hz. We showed that when the primary magnetic field is contaminated with random noises due to the presence of underwater metallic targets, water conductivity/frequency changes, and finite size of the transmitter, the performance of the full vector magnetic field tensor gradient approach degrades significantly compared to that of the DE method. In addition, the number of receivers required by the vector magnetic field tensor gradient technique and its sensitivity with respect to the sensor separation prevented us from further considering this technique for UW geo-location, leaving the non-linear approach, that uses only three vector Rx, as our technique of choice for tracking the location of underwater interrogation sensors with centimeter-level accuracy.

We report complex permittivity, conductivity, magnetic susceptibility, and attenuation for soils collected from a typical site in a current theater of operations. Our experimental setup consists of three network analyzers along with custombuilt sample holders and data reduction and analysis software. The sample holder has the advantage of large sample volume and a resulting higher signal to noise ratio. This system was developed to determine the electrical properties of soils over a wide frequency range from 100 Hz to 8 GHz. The lower frequencies are applicable to capacitive sensors for small shallow targets, while the higher frequencies are applicable to ground-penetrating radar (GPR) from 50 MHz to 2 GHz and beyond. S-parameter data is collected and reduced using a method, initially developed by Nicolson and Ross (1970)¹, for the determination of dielectric permittivity, magnetic permeability, and loss tangent from measured Sparameter data. Experimental results are compared with site geology and mineralogy. Applications include detection of tunnels, land mines, unexploded ordinance (UXO), concrete reinforcements, and other shallow compact targets.

Metal detector has commonly been used for landmine detection and ground-penetrating radar (GPR) is about to be deployed as dual sensor that is in combination with metal detector. Since both devices employ electromagnetic techniques, they are influenced by magnetic and dielectric properties of soil. To observe the influence, various soil properties as well as their spatial distributions were measured in four types of soil where a field test of metal detectors and GPRs took place. By analyzing soil properties these four types of soil were graded based on the estimated amount of influence on the detection techniques. The classification was compared to the detection performance of devices obtained from the blind test and a clear correlation between the difficulty of soil and the performance was observed; the detection and identification performance were degraded in soils that were classified as problematic. Therefore, it was demonstrated that the performance of metal detector and GPR for landmine detection can qualitatively be assessed by geophysical analyses.

In this study, we present generation of Strip-map Synthetic Aperture Radar (SAR) images using impulse GPR system, and investigate effects of different soil types on SAR images. The SAR images of buried objects have been interpreted via 2D inverse Fourier transformation. GPR buried target data have been collected from three soil pools having different dielectric constants and B-scan images have been reconstructed from the received data using mean A-scan signal subtraction method. In order to reconstruct SAR images, the time domain data collected from multiple observation points have been transformed to 2D spectral domain. Non-uniform data have been interpolated over spatial Cartesian grid by using uniform interval. Thus, the SAR images have been reconstructed via 2D inverse FFT of interpolated data on k_y-k_z plane. When examined mathematical background of SAR algorithm, the values of different dielectric constants change the wave number of k. This can lead to deterioration of the SAR imagery. In this study, we investigate the Effect of the dielectric constant of different soils has been examined on SAR images. Finally, resolution difference between background removed B-Scan data and SAR images is considered.

Soil moisture conditions have an impact upon virtually all aspects of Army activities and are increasingly affecting its systems and operations. Soil moisture conditions affect operational mobility, detection of landmines and unexploded ordinance, natural material penetration/excavation, military engineering activities, blowing dust and sand, watershed responses, and flooding. This study further explores a method for high-resolution (2.7 m) soil moisture mapping using remote satellite optical imagery that is readily available from Landsat and QuickBird. The soil moisture estimations are needed for the evaluation of IED sensors using the Countermine Simulation Testbed in regions where access is difficult or impossible. The method has been tested in Helmand Province, Afghanistan, using a Landsat7 image and a QuickBird image of April 23 and 24, 2009, respectively. In previous work it was found that Landsat soil moisture can be predicted from the visual and near infra-red Landsat bands1-4. Since QuickBird bands 1-4 are almost identical to Landsat bands 1- 4, a Landsat soil moisture map can be downscaled using QuickBird bands 1-4. However, using this global approach for downscaling from Landsat to QuickBird scale yielded a small number of pixels with erroneous soil moisture values. Therefore, the objective of this study is to examine how the quality of the downscaled soil moisture maps can be improved by using a data stratification approach for the development of downscaling regression equations for each landscape class. It was found that stratification results in a reliable downscaled soil moisture map with a spatial resolution of 2.7 m.

Nuclear based explosive inspection techniques can detect a wide range of substances of importance for a wide range of objectives. For national and international security it is mainly the detection of nuclear materials, explosives and narcotic threats. For Customs Services it is also cargo characterization for shipment control and customs duties. For the military and other law enforcement agencies it could be the detection and/or validation of the presence of explosive mines, improvised explosive devices (IED) and unexploded ordnances (UXO). The inspection is generally based on the nuclear interactions of the neutrons (or high energy photons) with the various nuclides present and the detection of resultant characteristic emissions. These can be discrete gamma lines resulting from the thermal neutron capture process (n,γ) or inelastic neutron scattering (n,n'γ) occurring with fast neutrons. The two types of reactions are generally complementary. The capture process provides energetic and highly penetrating gamma rays in most inorganic substances and in hydrogen, while fast neutron inelastic scattering provides relatively strong gamma-ray signatures in light elements such as carbon and oxygen. In some specific important cases unique signatures are provided by the neutron capture process in light elements such as nitrogen, where unusually high-energy gamma ray is produced. This forms the basis for key explosive detection techniques. In some cases the elastically scattered source (of mono-energetic) neutrons may provide information on the atomic weight of the scattering elements. The detection of nuclear materials, both fissionable (e.g., ²³⁸U) and fissile (e.g., ²³⁵U), are generally based on the fissions induced by the probing neutrons (or photons) and detecting one or more of the unique signatures of the fission process. These include prompt and delayed neutrons and gamma rays. These signatures are not discrete in energy (typically they are continua) but temporally and energetically significantly different from the background, thus making them readily distinguishable. The penetrability of neutrons as probes and signatures as well as the gamma ray signatures make neutron interrogation applicable to the inspection of large conveyances such as cars, trucks, marine containers and also smaller objects like explosive mines concealed in the ground. The application of nuclear interrogation techniques greatly depends on operational requirements. For example explosive mines and IED detection clearly require one-sided inspection, which excludes transmission based inspection (e.g., transmission radiography) and greatly limits others. The technologies developed over the last decades are now being implemented with good results. Further advances have been made over the last several years that increase the sensitivity, applicability and robustness of these systems. The principle, applications and status of neutron-based inspection techniques will be reviewed.

A novel portable and autonomous X-ray dual-energy Radioscopy equipment, developed for bomb squad interventions and NDT applications and capable of in-situ digital radiography imaging with measurement of the effective Atomic number of materials (Z_eff), is presented. The system consists of a 2D dual-energy X-ray detector based on a rapidly translated linear array, a portable X-ray source and dedicated software running on a laptop or tablet PC. By measurement of the collected x-ray intensities at two different energy spectra, the system can directly compute the material Z_eff value for various organic materials contained in the scanned object and then identify them from a database list. The entire system calibration has been obtained using explosive simulants with known Z_eff values, the measurement error of Z_effbeing around ±3.5 % with respect to the reference values. The excellent image resolution and the ability of the automated threat identification algorithm are presented for experiments with a briefcase and a hand-held baggage having various domestic objects and an explosive simulant inside.

Defence R&D Canada - Suffield has conducted research and development on nuclear methods for detection of bulk explosives since 1994. Initial efforts were directed at confirmation of the presence of bulk explosives in land mines and improvised explosive devices (IEDs). In close collaboration with a few key Canadian companies, methods suitable for vehicle-mounted or fixed position applications and those suitable for person- or small robotportable roles have been studied. Vehicle-mounted systems mainly employ detection of characteristic radiation, whereas person-portable systems use imaging of back scattered radiation intensity distributions. Two key design tenets have been reduction of personnel shielding by the use of teleoperation and custom design of sensors to address the particular problem, rather than adapting an existing sensor to a problem. This is shown in a number of recent research examples. Among vehicle-mounted systems, recent research to improve the thermal neutron analysis (TNA) sensors, which were put into service with the Canadian Forces in 2002, are discussed. Research on fast neutron analysis (FNA) and associated particle imaging (API), which can augment or replace TNA, depending on the application, are described. Monoenergetic gamma ray induced photoneutron spectroscopy is a novel method which has a number of potential advantages and disadvantages over TNA and FNA. Sources, detectors and geometries have been identified and modelling studies have suggested feasibility. Among person-portable systems, research on neutron backscatter imaging and X-ray coded aperture backscatter imaging are discussed.

Nuclear Quadrupole Resonance (NQR) is a spectroscopic technique closely related to Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI). These techniques, and NQR in particular, induce signals from the material being interrogated that are very specific to the chemical and physical structure of the material, but are relatively insensitive to the physical form of the material. NQR explosives detection exploits this specificity to detect explosive materials, in contrast to other well known techniques that are designed to detect explosive devices. The past two decades have seen a large research and development effort in NQR explosives detection in the United States aimed at transportation security and military applications. Here, I will briefly describe the physical basis for NQR before discussing NQR developments over the past decade, with particular emphasis on landmine detection and the use of NQR in combating IED's. Potential future directions for NQR research and development are discussed.

Accurate and timely detection of explosives, energetic materials, and their associated compounds would provide valuable information to military commanders in a wide range of military operations: protection of fast moving convoys from mobile or static IED threats; more deliberate countermine and counter-IED operations during route or area clearance; and static roles such as hasty or deliberate checkpoints, critical infrastructure protection and support to public security. The detection of hidden explosive hazards is an extremely challenging problem, as evidenced by the fact that related research has been ongoing in many countries for at least seven decades and no general purpose solution has yet been found. Technologies investigated have spanned all major scientific fields, with emphasis on the physical sciences, life sciences, engineering, robotics, computer technology and mathematics. This paper will present a limited, operationally-focused overview of the current status of detection technologies. Emphasis will be on those technologies that directly detect the explosive hazard, as opposed to those that detect secondary properties of the threat, such as the casing, associated wires or electronics. Technologies that detect explosives include those based on nuclear radiation and terahertz radiation, as well as trace and biological detection techniques. Current research areas of the authors will be used to illustrate the practical applications.

We have previously reported results from a human-portable system using neutron interrogation to detect contraband and explosives. We summarized our methodology for distinguishing threat materials such as narcotics, C4, and mustard gas in the myriad of backgrounds present in the maritime environment. We are expanding our mission for the Domestic Nuclear Detection Office (DNDO) to detect Special Nuclear Material (SNM) through the detection of multiple fission signatures without compromising the conventional threat detection performance. This paper covers our initial investigations into using neutrons from compact pulsed neutron generators via the d(D,n)³He or d(T,n)α reactions with energies of ~2.5 and 14 MeV, respectively, for explosives (and other threats) detection along with a variety of gamma-ray detectors. Fast neutrons and thermal neutrons (after successive collisions) can stimulate the emission of various threat detection signatures. For explosives detection, element-specific gamma-ray signatures via the (n,n'γ) inelastic scattering reaction and the (n,'γ) thermal capture reaction are detected. For SNM, delayed gamma-rays following fission can be measured with the same detector. Our initial trade-off investigations of several gamma-ray detectors types (NaI, CsI, LaBr₃, HPGe) for measuring gamma-ray signatures in a pulsed neutron environment for potential application in a human-portable active interrogation system are covered in this paper.

Existing terahertz THz systems for detecting concealed explosives are not capable of identifying explosive type which leads to higher false alarm rates. Moreover, some of those systems are imaging systems that invade personal privacy, and require more processing and computational resources. Other systems have no polarization preference which makes them incapable of capturing the geometric features of an explosive. In this study a non-imaging polarized THz passive system for detecting and identifying concealed explosives overcoming the forgoing shortcomings is developed. The system employs a polarized passive THz sensor in acquiring emitted data from a scene that may have concealed explosives. The acquired data are decomposed into their natural resonance frequencies, and the number of those frequencies is used as criteria in detecting the explosive presence. If the presence of an explosive is confirmed, a set of physically based retrieval algorithms is used in extracting the explosive dielectric constant/refractive index value from natural resonance frequencies and amplitudes of associated signals. Comparing the refractive index value against a database of refractive indexes of known explosives identifies the explosive type. As an application, a system having a dual polarized radiometer operating within the frequency band of 0.62- 0.82 THz is presented and used in detecting and identifying person borne C-4 explosive concealed under a cotton garment. The system showed higher efficiencies in detecting and identifying the explosive.

We describe experiments where different explosives were hidden under common barrier materials, and THz radiation was used to detect and identify these explosives. Our THz system, a time-domain spectroscopy (TDS) system, is based on a femtosecond laser whose radiation is converted into THz radiation by a low-temperature grown GaAs photoconductive switch. A similar switch detects the reflected signal. The advantage of using a TDS system is that pulses reflected from the barrier and the actual explosive, arrive at different instances at the detector. This simplifies the separation of the barrier signature from the explosive signature, compared to a frequency domain system. However, partial temporal overlap between the two pulses makes it challenging to completely separate the spectral characteristics of the explosive from the characteristics of the barrier. Also, in addition to attenuating the THz-pulses, transmission through barrier materials may add spectral features to the reflected signal, hampering recognition of the explosive. On top of that, the explosive may have a rough surface, which reduces the strength of the reflected signal. In this contribution we shall address these issues and discuss strategies that may be used to face these challenges.

A multispectral imaging technique has been developed to detect and identify explosive particles, e.g. from a fingerprint, at stand-off distances using Raman spectroscopy. When handling IED's as well as other explosive devices, residues can easily be transferred via fingerprints onto other surfaces e.g. car handles, gear sticks and suite cases. By imaging the surface using multispectral imaging Raman technique the explosive particles can be identified and displayed using color-coding. The technique has been demonstrated by detecting fingerprints containing significant amounts of 2,4-dinitrotoulene (DNT), 2,4,6-trinitrotoulene (TNT) and ammonium nitrate at a distance of 12 m in less than 90 seconds (22 images × 4 seconds)¹. For each measurement, a sequence of images, one image for each wave number, is recorded. The spectral data from each pixel is compared with reference spectra of the substances to be detected. The pixels are marked with different colors corresponding to the detected substances in the fingerprint. The system has now been further developed to become less complex and thereby less sensitive to the environment such as temperature fluctuations. The optical resolution has been improved to less than 70 μm measured at 546 nm wavelength. The total detection time is ranging from less then one minute to around five minutes depending on the size of the particles and how confident the identification should be. The results indicate a great potential for multi-spectral imaging Raman spectroscopy as a stand-off technique for detection of single explosive particles.

Portendo has in collaboration with FOI, the Swedish Defence Research Agency, developed a world-leading technique of trace detection of explosives at standoff distance using Raman spectroscopy. The technology is further developed in order to enhance the sensitivity of the method and to be able to extend the field of applications. Raman scattering is a well-established technique able to detect substances down to single micrograms at standoff distances, however, one of the obstacles limiting the detection possibilities is interfering fluorescence, originating either from the substance itself or from the surrounding material. One main challenge for this technology is thus to either omit the excitation of the fluorescent process altogether or to be able to separate the two processes and only detect the Raman signal. Due to the difference in the temporal behavior of the two processes - Raman scattering occurs in the order of femtoseconds while fluorescence typically has a lifetime in the order of nanoseconds - one way to theoretically separate them is to limit the measurement to as short time as possible, cutting off most of the emitted fluorescence. The improvement depends on how much of the fluorescence can be omitted without decreasing the Raman signal. Experimentally, we have verified this improvement in signal to noise ratio when using a laser with picosecond pulses instead of nanosecond pulses, which has resulted in an improvement in SNR of up to 7 times for bulk ANFO. These results verify the predicted signal enhancement and suggest higher sensitivity for standoff detection in future systems.

A database of vehicle mounted GPR mine data acquired over rough road conditions is analyzed to determine the sensitivity to random and rapid variations in antenna heights due to naturally occurring antenna bounce. Results are also described for data acquired during tests designed to induce specific bounce profiles during data collections. Significant increases are observed in false alarms, and significant decreases are observed in mine detection probabilities at rougher test lane locations. Perhaps the most significant antenna height factor was discovered to be rapid and large GPR amplitude changes due to the variations in antenna height and thus propagation loss. Finally, normalization techniques developed by AARD are examined to correct for these GPR antenna bounce factors and are shown to be effective in most cases, particularly for some of the more severe induced bounce scenarios.

Buried explosives have proven to be a challenging problem for which ground penetrating radar (GPR) has shown to be effective. This paper discusses an explosive hazard detection algorithm for forward looking GPR (FLGPR). The proposed algorithm uses the fast Fourier transform (FFT) to obtain spectral features of anomalies in the FLGPR imagery. Results show that the spectral characteristics of explosive hazards differ from that of background clutter and are useful for rejecting false alarms (FAs). A genetic algorithm (GA) is developed in order to select a subset of spectral features to produce a more generalized classifier. Furthermore, a GA-based K-Nearest Neighbor probability density estimator is employed in which targets and false alarms are used as training data to produce a two-class classifier. The experimental results of this paper use data collected by the US Army and show the effectiveness of spectrum based features in the detection of explosive hazards.

This paper proposes an effective anomaly detection algorithm for a forward-looking ground-penetrating radar(FLGPR). One challenge for threat detection using FLGPR is its high dynamic range in response to different kinds of targets and clutter objects. The application of a fixed threshold for detection in a full-band radar image often yields a large number of false alarms. We propose a method that uses both narrow-band and full-band radar processing, coupled with a classifier that uses complex-valued Gabor filter responses as the features. We then fuse the narrow-band and fullband images into a composite confidence map and detect local maxima in this map to produce candidate alarm locations. Full-band radar images provide a high degree of image resolution, while narrow-band images provide a means to detect targets which have a unique narrow-band signature. Experimental results for our improved detection techniques are demonstrated on data sets collected at a US Army test site.

A method for segmenting deformable shapes of soil and ground layers in successive GPR image frames is described in this paper. First, pre-processing operators are applied to enhance the quality of each image frame. Second, local histogram features are used to initialize membership probabilities of each pixel in the current image frame. Then, a segmentation algorithm based on relaxation labeling is applied to perform image segmentation. This algorithm uses information from previous and current image frames to perform layer identification by formulating the segmentation task as a probabilistic relaxation labeling process in which the current frame image is used for initializing pixel membership probabilities estimated from gray-level histograms. The previous frame image is used for estimating the compatibility values to be utilized for segmenting the current frame image using mutual information among neighboring pixels. By iteratively refining the membership probabilities of each pixel in the current image frame in parallel, an enhanced segmentation is produced according to the refined probabilities. A distinguishing characteristic of this process is the ability to incorporate both temporal contexts (down-track history information encoded as compatibilities) and spatial contexts (current-scan pixel neighborhood information encoded as probabilities), concurrently. The segmented image is post-processed by further filtering operations and checking for highly unlikely decisions to produce the final segmentation.

Rough surfaces present an impediment to the detection of buried threats with ground penetrating radar (GPR). Besides introducing artifacts in the sub-surface due to rough scattering, very rough or uneven surfaces can make inference of the location of the ground response from GPR data difficult. Since many algorithms rely on the accurate localization of the air/ground interface, mistakes in ground location inference can cause significant increases in false alarm rates. Many different approaches to localizing the ground in a particular A-scan have been proposed, but sharing information across multiple A-scans to form a realistic, smoothly varying ground response over many spatial locations is a difficult problem that often requires computationally expensive approaches for adequate solutions. In this work we present an application of the well-known Viterbi algorithm for accurate localization of the air/ground interface based on hypothesized locations from multiple nearby A-scans. Our implementation of the Viterbi algorithm enables principled incorporation of prior information into the ground tracking framework, and provides a solution capable of adapting computational complexity to the severity of the ground localization problem. Furthermore, the Viterbi algorithm can act as a meta-algorithm, allowing the use of different A-scan based ground detectors as input, for example. This work illustrates how the Viterbi algorithm can be incorporated into pre-screening algorithms to provide improved target detection rates at lower false alarm rates.

In this paper, we propose a new method for performing ground-tracking using ground-penetrating radar (GPR). Ground-tracking involves identifying the air-ground interface, which is usually the dominant feature in a radar image but frequently is obscured or mimicked by other nearby elements. It is an important problem in landmine detection using vehicle-mounted systems because antenna motion, caused by bumpy ground, can introduce distortions in downtrack radar images, which ground-tracking makes it possible to correct. Because landmine detection is performed in real-time, any algorithm for ground-tracking must be able to run quickly, prior to other, more computationally expensive algorithms for detection. In this investigation, we first describe an efficient algorithm, based on dynamic programming, that can be used in real-time for tracking the ground. We then demonstrate its accuracy through a quantitative comparison with other proposed ground-tracking methods, and a qualitative comparison showing that its ground-tracking is consistent with human observations in challenging terrain.

In using GPR images for landmine detection it is often useful to identify the air-ground interface in the GRP signal for alignment purposes. A common simple technique for doing this is to assume that the highest return in an A-scan is from the reflection due to the ground and to use that as the location of the interface. However there are many situations, such as the presence of nose clutter or shallow sub-surface objects, that can cause the global maximum estimate to be incorrect. A Support Vector Data Description (SVDD) is a one-class classifier related to the SVM which encloses the class in a hyper-sphere as opposed to using a hyper-plane as a decision boundary. We apply SVDD to the problem of detection of the air-ground interface by treating each sample in an A-scan, with some number of leading and trailing samples, as a feature vector. Training is done using a set of feature vectors based on known interfaces and detection is done by creating feature vectors from each of the samples in an A-scan, applying the trained SVDD to them and selecting the one with the least distance from the center of the hyper-sphere. We compare this approach with the global maximum approach, examining both the performance on human truthed data and how each method affects false alarm and true positive rates when used as the alignment method in mine detection algorithms.

In using GPR images for landmine detection it is often useful to identify the air-ground interface in the GPR signal for alignment purposes. A number of algorithms have been proposed to solve the air-ground interface detection problem, including some which use only A-scan data, and others which track the ground in B-scans or C-scans. Here we develop a framework for comparing these algorithms relative to one another and we examine the results. The evaluations are performed on data that have been categorized in terms of features that make the air-ground interface difficult to find or track. The data also have associated human selected ground locations, from multiple evaluators, that can be used for determining correctness. A distribution is placed over each of the human selected ground locations, with the sum of these distributions at the algorithm selected location used as a measure of its correctness. Algorithms are also evaluated in terms of how they affect the false alarm and true positive rates of mine detection algorithms that use ground aligned data.

We discuss some results and observations on applying syntactic pattern recognition (SPR) methodology for landmine detection using impulse ground-penetrating radar (GPR). In the SPR approach, the GPR A-scans are first converted into binary-valued strings by inverse filtering, followed by concavity detection to identify the peaks and valleys representing the locations of impedance discontinuities in the return signal. During the training phase, the characteristic binary strings for a particular landmine are found by looking at all the exemplars of that mine and selecting the collection of strings that yield the best detection results on these exemplars. These characteristic strings can be detected very efficiently using finite state machines (FSMs). Finally, the FSM detections are clustered to assign confidence to each detection, and discard sparse detections. Provided that the impulse GPR provides enough resolution in range, the SPR method can be a robust and high-speed solution for landmine detection and classification, because it aims to exploit the impedance discontinuity profile of the target, which is a description of the internal material structure of the target and little affected by external clutter. To evaluate the proposed methodology, the SPR scheme is applied to a set of impulse GPR data taken at a government test site. We suggest that coherent frequency-agile radar may be a better option for the SPR approach, since it addresses some of the drawbacks of a non-coherent impulse GPR caused by internally non-coherent within-channel signals which necessitate non-coherent integration and its attendant longer integration times, and non-coherent adjacent channels which severely limit the ability to do spatial, or at a minimum, cross-range processing if the GPR is in a linear array antenna.

Syntactic landmine detection has been proposed to detect and classify non-metallic landmines using ground penetrating radar (GPR). In this approach, the GPR return is processed to extract characteristic binary strings for landmine and clutter discrimination. In our previous work, we discussed the preprocessing methodology by which the amplitude information of the GPR A-scan signal can be effectively converted into binary strings, which identify the impedance discontinuities in the signal. In this work, we study the statistical properties of the binary string space. In particular, we develop a Markov chain model to characterize the observed bit sequence of the binary strings. The state is defined as the number of consecutive zeros between two ones in the binarized A-scans. Since the strings are highly sparse (the number of zeros is much greater than the number of ones), defining the state this way leads to fewer number of states compared to the case where each bit is defined as a state. The number of total states is further reduced by quantizing the number of consecutive zeros. In order to identify the correct order of the Markov model, the mean square difference (MSD) between the transition matrices of mine strings and non-mine strings is calculated up to order four using training data. The results show that order one or two maximizes this MSD. The specification of the transition probabilities of the chain can be used to compute the likelihood of any given string. Such a model can be used to identify characteristic landmine strings during the training phase. These developments on modeling and characterizing the string statistics can potentially be part of a real-time landmine detection algorithm that identifies landmine and clutter in an adaptive fashion.

In this work the singular value decomposition (SVD) is used to analyze matrices of ground penetrating radar (GPR) data. The targets to be detected are Russian PMN antipersonnel landmines and improvised explosive devices constructed from 155mm artillery shells. Target responses are simulated with GPRmax 2D, a simulation package based on the Finite- Difference-Time-Domain method. First, the utility of the SVD for image enhancement and reconstruction is demonstrated. Then the singular values and singular vectors of the decomposed matrices are analyzed with the goal of finding properties that will aid in the development of automated underground detection algorithms.

Due to the large amount of data generated by vehicle-mounted ground penetrating radar (GPR) antennae arrays, advanced feature extraction and classification can only be performed on a small subset of data during real-time operation. As a result, most GPR based landmine detection systems implement "pre-screening" algorithms to processes all of the data generated by the antennae array and identify locations with anomalous signatures for more advanced processing. These pre-screening algorithms must be computationally efficient and obtain high probability of detection, but can permit a false alarm rate which might be higher than the total system requirements. Many approaches to prescreening have previously been proposed, including linear prediction coefficients, the LMS algorithm, and CFAR-based approaches. Similar pre-screening techniques have also been developed in the field of video processing to identify anomalous behavior or anomalous objects. One such algorithm, an online k-means approximation to an adaptive Gaussian mixture model (GMM), is particularly well-suited to application for pre-screening in GPR data due to its computational efficiency, non-linear nature, and relevance of the logic underlying the algorithm to GPR processing. In this work we explore the application of an adaptive GMM-based approach for anomaly detection from the video processing literature to pre-screening in GPR data. Results with the ARA Nemesis landmine detection system demonstrate significant pre-screening performance improvements compared to alternative approaches, and indicate that the proposed algorithm is a complimentary technique to existing methods.

It has been established throughout the ground-penetrating radar (GPR) literature that environmental factors can severely impact the performance of GPR sensors in landmine detection applications. Over the years, electromagnetic inversion techniques have been proposed for determining these factors with the goal of mitigating performance losses. However, these techniques are often computationally expensive and require models and responses from canonical targets, and therefore may not be appropriate for real-time route-clearance applications. An alternative technique for mitigating performance changes due to environmental factors is context-dependent classification, in which decision rules are adjusted based on contextual shifts identified from the GPR data. However, analysis of the performance of context-dependent learning has been limited to qualitative comparisons of contextually-similar GPR signatures and quantitative improvement to the ROC curve, while the actual information extracted regarding soils has not been investigated thoroughly. In this work, physics-based features of GPR data used in previous context-dependent approaches were extracted from simulated GPR data generated through Finite-Difference Time-Domain (FDTD) modeling. Statistical techniques where then used to predict several potential contextual factors, including soil dielectric constant, surface roughness, amount of subsurface clutter, and the existence of subsurface layering, based on the features. Results suggest that physics-based features of the GPR background may contain informatin regarding physical properties of the environment, and contextdependent classification based on these features can exploit information regarding these potentially-important environmental factors.

Ground Penetrating Radar (GPR) data provides a powerful technique to identify subsurface buried threats. Although GPR data contains a three-dimensional representation of the subsurface, object truth (i.e. labels and positions of true threat objects in training lanes) is often provided in only two dimensions (GPS coordinates along the earth's surface). To mitigate uncertainty in an object's location in depth, many successful feature extraction/ object recognition techniques in GPR extract feature vectors from several depth regions, and attempt to combine information across these feature vectors to make final decisions. However, many machine learning techniques are not well suited for learning under these conditions. Multiple Instance Learning (MIL) is a type of supervised learning method in which labels are available for sets of samples, but not for individual samples. The goal of learning in MIL is to classify new sets of samples as they become available. This set-based framework is useful in processing GPR responses since features are often extracted independently from multiple un-labeled depth bins, and thus a set of features is produced at each potential threat location. In this work, a comparison of several previous approaches to MlL applied to landmine detection in GPR data is presented. One recent algorithm, the p-Posterior Mixture Model approach (pPMM) is given special attention, and several slight modifications to the pPMM approach are presented and compared.

In landmine detection applications, fluctuation of environmental and operating conditions can limit the performance of sensors based on ground-penetrating radar (GPR) technology. As these conditions vary, the classification and fusion rules necessary for achieving high detection and low false alarm rates may change. Therefore, context-dependent learning algorithms that exploit contextual variations of GPR data to alter decision rules have been considered for improving the performance of landmine detection systems. Past approaches to contextual learning have used both generative and discriminative methods to learn a probabilistic mixture of contexts, such as a Gaussian mixture, fuzzy c-means clustering, or a mixture of random sets. However, in these approaches the number of mixture components is pre-defined, which could be problematic if the number of contexts in a data collection is unknown a priori. In this work, a generative context model is proposed which requires no a priori knowledge in the number of mixture components. This was achieved through modeling the contextual distribution in a physics-based feature space with a Gaussian mixture, while also incorporating a Dirichlet process prior to model uncertainty in the number of mixture components. This Dirichlet process Gaussian mixture model (DPGMM) was then incorporated in the previously-developed Context-Dependent Feature Selection (CDFS) framework for fusion of multiple landmine detection algorithms. Experimental results suggest that when the DPGMM was incorporated into CDFS, the degree of performance improvement over conventional fusion was greater than when a conventional fixed-order context model was used.

Ground-penetrating radar (GPR) sensors provide an effective means for detecting changes in the sub-surface electrical properties of soils, such as changes indicative of landmines or other buried threats. However, most GPR-based pre-screening algorithms only localize target responses along the surface of the earth, and do not provide information regarding an object's position in depth. As a result, feature extraction algorithms are forced to process data from entire cubes of data around pre-screener alarms, which can reduce feature fidelity and hamper performance. In this work, spectral analysis is investigated as a method for locating subsurface anomalies in GPR data. In particular, a 2-D spatial/frequency decomposition is applied to pre-screener flagged GPR B-scans. Analysis of these spatial/frequency regions suggests that aspects (e.g. moments, maxima, mode) of the frequency distribution of GPR energy can be indicative of the presence of target responses. After translating a GPR image to a function of the spatial/frequency distributions at each pixel, several image segmentation approaches can be applied to perform segmentation in this new transformed feature space. To illustrate the efficacy of the approach, a performance comparison between feature processing with and without the image segmentation algorithm is provided.

In this study, we provide an extensive comparison of different clutter suppression techniques that are proposed to enhance ground penetrating radar (GPR) data. Unlike previous studies, we directly measure and present the effect of these preprocessing algorithms on the detection performance. Basic linear prediction algorithm is selected as the detection scheme and it is applied to real GPR data after applying each of the available clutter suppression techniques. All methods are tested on an extensive data set of different surrogate mines and other objects that are commonly encountered under the ground. Among several algorithms, singular value decomposition based clutter suppression stands out with its superior performance and low computational cost, which makes it practical to use in real-time applications.

We demonstrate the development and use of novel image processing methods to combine dual-band (MWIR and LWIR) images from SELEX GALILEO's Condor II camera to extract characteristics of observed scenes comprising buried mines and explosive objects. We discuss the development of a statistical processing technique to extract the different characteristics of the two bands. We further present a statistical classifier used to detect targets on independently trained images with a high detection probability and low false negative rates and discuss methods to mitigate the impact of false positives through the selective processing of image regions and the contextual interpretation of the scene content.

Advances in hyperspectral sensors and algorithms may benefit the detection of person-borne improvised explosive devices (PB-IEDs). While portions of the electromagnetic spectrum, such as the x-ray and terahertz regions, have been investigated for this application, the spectral region of the ultraviolet (UV) through shortwave infrared (SWIR) (250 nm to 2500 nm) has received little attention. The purpose of this work was to investigate what, if any, potential there may be for exploiting the spectral region of the UV through SWIR for the detection of hidden objects under the clothing of individuals. The optical properties of both common fabrics and materials potentially used to contain threat objects were measured, and a simple example using a hyperspectral imager is provided to illustrate the combined effect. The approach, measurement methods, and results are described in this paper, and the potential for hyperspectral imaging is addressed.

The burial of objects disturbs the ground surface in visually perceptible ways. This project investigated how such information can inform detection via imaging from visible through mid-infrared wavelengths. Images of the ground surface where objects were buried were collected at multiple visible through mid-infrared wavelengths prior to burial and afterward at intervals spanning approximately two weeks. Signs of soil disturbed by emplacement change over time and exposure in the natural environment and vary in salience across wavelengths for different time periods. Transient cues related to soil moisture or illumination angle can make signatures extraordinarily salient under certain conditions. Longpass shortwave infrared and multi-band mid-infrared imaging can enhance the signature of disturbed soils over visible imaging. These findings add knowledge and understanding of how soil disturbances phenomena can be exploited to aid detection.

Burying objects below the ground can potentially alter their thermal properties. Moreover, there is often soil disturbance associated with recently buried objects. An intensity video frame image generated by an infrared camera in the medium and long wavelengths often locally varies in the presence of buried explosive hazards. Our approach to automatically detecting these anomalies is to estimate a background model of the image sequence. Pixel values that do not conform to the background model may represent local changes in thermal or soil signature caused by buried objects. Herein, we present a Gaussian mixture model-based technique to estimate the statistical model of background pixel values. The background model is used to detect anomalous pixel values on the road while a vehicle is moving. Foreground pixel confidence values are projected into the UTM coordinate system and a UTM confidence map is built. Different operating levels are explored and the connected component algorithm is then used to extract islands that are subjected to size, shape and orientation filters. We are currently using this approach as a feature in a larger multi-algorithm fusion system. However, in this article we also present results for using this algorithm as a stand-alone detector algorithm in order to further explore its value in detecting buried explosive hazards.

In this paper we describe a method for generating cues of targets present in the field-of-view of an infrared (IR) camera installed on a moving vehicle. A typical two class classifier requires the building of an image library containing manually extracted examples of both types of objects, i.e., targets and non-targets. This approach, usually tedious on static images, becomes intractable when using video sequences taken from a moving vehicle. To avoid a detailed manual segmentation of video sequences, we employ a multiple instance learning (MIL) framework that allows training a two class classifier just by specifying if a frame contains targets or not. The proposed method has three steps. First, for each frame of a training run, we generate a set of possible points of interest using a corner detection algorithm. Second, for the same training run, we tag each frame as positive (target hits present) or negative (only non-target hits present). Each hit is described using the local binary pattern (LPB) features computed around its image location. The generated LPB feature vectors, together with their frame tag, are used by the MIL training framework to generate a set of target LPB prototypes. Although many regular classifiers may be trained using MIL, in this paper we employed a simple approach based on the nearest prototype. In the last step, we used the computed prototypes to classify the corner hits detected in several test video sequences. To validate our approach, we present results obtained on several runs gathered with a long wave infrared (LWIR) camera mounted on a moving vehicle.

Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buried objects that threaten the lives of Soldiers and civilians. However, while neither GPR nor EMI sensing alone can provide optimal detection across all target types, the two approaches are highly complementary. As a result, many landmine systems seek to make use of both sensing modalities simultaneously and fuse the results from both sensors to improve detection performance for targets with widely varying metal content and GPR responses. Despite this, little work has focused on large-scale comparisons of different approaches to sensor fusion and machine learning for combining data from these highly orthogonal phenomenologies. In this work we explore a wide array of pattern recognition techniques for algorithm development and sensor fusion. Results with the ARA Nemesis landmine detection system suggest that nonlinear and non-parametric classification algorithms provide significant performance benefits for single-sensor algorithm development, and that fusion of multiple algorithms can be performed satisfactorily using basic parametric approaches, such as logistic discriminant classification, for the targets under consideration in our data sets.

Forward looking video can provide a large amount of tactically-relevant information to vehicle operators regarding roadside explosive threats. However it is difficult for vehicle operators to keep track of what roadside objects have changed since their last excursion, or what new objects have appeared on a road and might therefore contain an explosive threat. Furthermore, the large amount of data generated by forward looking video can overwhelm users. It would be of benefit to vehicle operators if only objects that had significantly changed since a recent excursion were flagged and presented to the user. In this work we develop techniques for video and ground-penetrating radar (GPR) tracking and aligning, and novel-object identification for application in route clearance patrols. We focus on aligning video data collected using vehicle mounted forward-looking video cameras and downward-looking GPR using locallyinvariant feature transforms and set-based distance metrics. Based on these aligned image streams, we then apply pattern classification approaches to discriminate new explosive threats from stationary and persistent objects. The techniques described in this work are widely applicable to other forward and downward-looking sensor systems, and are computationally tractable. The results indicate the potential to robustly identify recently changed roadside threats, and to present a significantly reduced amount of information to end-users for further operational analysis.

In this article, we propose a method to fuse multiple algorithms in a long wave infrared (LWIR) system in the context of forward looking buried explosive hazard detection. A pre-screener is applied first, which is an ensemble of local RX filters and mean shift clustering in UTM space. Hit correspondence is then performed with an algorithm based on corner detection, local binary patterns (LBP), multiple instance learning (MIL) and mean shift clustering in UTM space. Next, features from image chips are extracted from UTM confidence maps based on maximally stable extremal regions (MSERs) and Gaussian mixture models (GMMs). These sources are then fused using an ordered weighted average (OWA). While this fusion approach has yet to improve the overall positive detection rate in LWIR, we do demonstrate false alarm reduction. Targets that are not detected by our system are also not detected by a human under visual inspection. Experimental results are shown based on field data measurements from a US Army test site.

Spectral, shape or texture features of the detected targets are used to model the likelihood of the targets to be potential mines in a minefield. However, some potential mines can be false alarms due to the similarity of the mine signatures with natural and other manmade clutter signatures. Therefore, in addition to the target features, spatial distribution of the detected targets can be used to improve the minefield detection performance. In our recently published SPIE paper, we evaluated minefield detection performance for both patterned and unpatterned minefields in highly cluttered environments, simultaneously using both target features and target spatial distributions that define Markov Marked Point Process (MMPP). The results have suggested that proper exploitation of spectral/shape features and spatial distributions can indeed contribute improved performance of patterned and unpatterned minefield detection. Also, the ability of the algorithm to detect the minefields in highly cluttered environments shows the robustness of the developed minefield detection algorithm based on MMPP formulation. Moreover, the results show that the MMPP minefield detection algorithm performs significantly better than the baseline algorithm employing spatial point process with false alarm mitigation. Since these results were based on the simulated data, it is not clear that the MMPP detection algorithm has fully achieved its best performance. To validate its performance, an analytical solution for the minefield detection problem will be developed, and its performance will be compared with the performance of the simulated solution. The analytical solution for the complete minefield detection problem is intractable due to a large number of detections and the variation of the number of detected mines in the minefield process. Therefore, an analytical solution for a simplified detection problem will be derived, and its minefield performance will be compared with the minefield performance obtained from the simulation in the same MMPP framework for different clutter rates.

Typical classification models used for detection of buried landmines estimate a singular discriminative output. This classification is based on a model or technique trained with a given set of training data available during system development. Regardless of how well the technique performs when classifying objects that are 'similar' to the training set, most models produce undesirable (and many times unpredictable) responses when presented with object classes different from the training data. This can cause mines or other explosive objects to be misclassified as clutter, or false alarms. Bayesian regression and classification models produce distributions as output, called the predictive distribution. This paper will discuss predictive distributions and their application to characterizing uncertainty in the classification decision, from the context of landmine detection. Specifically, experiments comparing the predictive variance produced by relevance vector machines and Gaussian processes will be described. We demonstrate that predictive variance can be used to determine the uncertainty of the model in classifying an object (i.e., the classifier will know when it's unable to reliably classify an object). The experimental results suggest that degenerate covariance models (such as the relevance vector machine) are not reliable in estimating the predictive variance. This necessitates the use of the Gaussian Process in creating the predictive distribution.

Trainable size-contrast filters, similar to local dual-window RX anomaly detectors, utilizing the Bhattacharyya distance are used to detect buried explosive hazards in forward-looking long-wave infrared imagery. The imagery, captured from a moving vehicle, is geo-referenced, allowing projection of pixel coordinates into (UTM) Universal Transverse Mercator coordinates. Size-contrast filter detections for a particular frame are projected into UTM coordinates, and peaks are detected in the resulting density using the mean-shift algorithm. All peaks without a minimum number of detections in their local neighborhood are discarded. Peaks from individual frames are then combined into a single set of tentative hit locations, and the same mean-shift procedure is run on the resulting density. Peaks without a minimum number of hit locations in their local neighborhood are removed. The remaining peaks are declared as target locations. The mean-shift steps utilize both the spatial and temporal information in the imagery. Scoring is performed using ground truth locations in UTM coordinates. The size-contrast filter and mean-shift parameters are learned using a genetic algorithm which minimizes a multiobjective fitness function involving detection rate and false alarm rate. Performance of the proposed algorithm is evaluated on multiple lanes from a recent collection at a US Army test site.