Two vehicle mounted metal detector arrays are used in conjunction to perform object classification. The first array (Vallon
VMV-16) contains small coils for detecting shallow targets. The second array (Minelab STMR II) contains receive coils
of roughly the same size, but a single large transmitter for detecting deep targets. These two sensors are used together to
classify objects as: "SHALLOW and LARGE","DEEP and LARGE", or "SHALLOW and SMALL". SHALLOW/DEEP
implies the depth of the object; SMALL/LARGE implies the metal content. These object classes are further specified
within the paper. An experiment is performed using unexploded ordnance (UXO) and shallow buried calibration objects.
The UXO ranges in depth from flush buried to 48". The calibration targets consist of metallic cylinders ranging in depth
from flush buried to 12". The strength of each sensor is described and a fusion algorithm is developed. A detection
performance curve is shown illustrating the benefit of multi-sensor fusion for UXO detection.
This paper reports vehicle based electromagnetic induction (EMI) array sensor data inversion and discrimination results. Recent field
studies show that EMI arrays, such as the Minelab Single Transmitter Multiple Receiver (STMR), and the Geophex GEM-5 EMI
array, provide a fast and safe way to detect subsurface metallic targets such as landmines, unexploded ordnance (UXO) and buried
explosives. The array sensors are flexible and easily adaptable for a variety of ground vehicles and mobile platforms, which makes
them very attractive for safe and cost effective detection operations in many applications, including but not limited to explosive
ordnance disposal and humanitarian UXO and demining missions. Most state-of-the-art EMI arrays measure the vertical or full vector
field, or gradient tensor fields and utilize them for real-time threat detection based on threshold analysis. Real field practice shows that
the threshold-level detection has high false alarms. One way to reduce these false alarms is to use EMI numerical techniques that are
capable of inverting EMI array data in real time. In this work a physically complete model, known as the normalized volume/surface
magnetic sources (NV/SMS) model is adapted to the vehicle-based EMI array, such as STMR and GEM-5, data. The NV/SMS model
can be considered as a generalized volume or surface dipole model, which in a special limited case coincides with an infinitesimal
dipole model approach. According to the NV/SMS model, an object's response to a sensor's primary field is modeled mathematically
by a set of equivalent magnetic dipoles, distributed inside the object (i.e. NVMS) or over a surface surrounding the object (i.e.
NSMS). The scattered magnetic field of the NSMS is identical to that produced by a set of interacting magnetic dipoles. The
amplitudes of the magnetic dipoles are normalized to the primary magnetic field, relating induced magnetic dipole polarizability and
the primary magnetic field. The magnitudes of the NSMS are determined directly by minimizing the difference between measured and
modeled data for any known object and any type of EMI sensor data. The EMI array data are inverted via the combined NV/SMS and
differential evolution inversion method that uses a search scheme to estimate the location of the target. First, the applicability of the
NV/SMS and DE algorithms to STMR and GEM-5 data sets is demonstrated by comparing the modeled data against the actual data,
and finally the discrimination studies are conducted using as discrimination parameters the total NV/SMS and the principal axis of the induced magnetic polarizability tensor for each target.
A novel outdoor synthetic aperture acoustic (SAA) system consists of a microphone and loudspeaker traveling along a
6.3-meter rail system. This is an extension from a prior indoor laboratory measurement system in which selected targets
were insonified while suspended in air. Here, the loudspeaker and microphone are aimed perpendicular to their direction
of travel along the rail. The area next to the rail is insonified and the microphone records the reflected acoustic signal,
while the travel of the transceiver along the rail creates a synthetic aperture allowing imaging of the scene. Ground
surfaces consisted of weathered asphalt and short grass. Several surface-laid objects were arranged on the ground for
SAA imaging. These included rocks, concrete masonry blocks, grout covered foam blocks; foliage obscured objects and
several spherical canonical targets such as a bowling ball, and plastic and metal spheres. The measured data are
processed and ground targets are further analyzed for characteristics and features amenable for discrimination. This
paper includes a description of the measurement system, target descriptions, synthetic aperture processing approach and
preliminary findings with respect to ground surface and target characteristics.
State-of-the-art electromagnetic induction (EMI) arrays provide significant capability enhancement to landmine,
unexploded ordnance (UXO), and buried explosives detection applications. Arrays that are easily configured for
integration with a variety of mobile platforms offer improved safety and efficiency to personnel conducting detection
operations including site remediation, explosive ordnance disposal, and humanitarian demining missions. We present
results from an evaluation of two vehicle-based frequency domain EMI arrays. Our research includes implementation of
a simple circuit model to estimate target location from sensor measurements of the scattered vertical magnetic field
component. Specifically, we characterize any conductive or magnetic target using a set of parameters that describe the
eddy current and magnetic polarizations induced about a set of orthogonal axes. Parameter estimations are based on the
fundamental resonance mode of a series inductance and resistance circuit. This technique can be adapted to a variety of
EMI array configurations, and thus offers target localization capabilities to a number of applications.
Proc. SPIE. 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV
KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Metals, Sensor performance, Electromagnetic coupling, Unexploded object detection, Ground penetrating radar, Land mines, General packet radio service
Metal detectors and ground penetrating radar have become the standard sensors for buried landmine and UXO detection.
Joint systems have existed since the late 90s. Recent system development has again led to the placement of MD and GPR
sensors on ground vehicles for detection of in-road landmine and UXO objects. In this work, two prominent systems - one
GPR and one metal detector - are operated on a test site populated with landmine and deep buried UXO. The strength of
the GPR is the ability to detect plastic cased landmines while the strength of the metal detector is to detect deep buried
UXO. The sensors' capabilities overlap in regards to metal cased landmines. A simple fusion approach is used to show
how these two sensors can be used together to create a platform that carries the strengths of both sensors. The final alarm
list averages the confidence values produced by each sensor. ROC curves are used to quantify the performance. Curves are
presented for each sensor standing alone and for their fusion performance.
This paper looks at depth estimation techniques using electromagnetic induction (EMI) metal detectors. Four algorithms are considered. The first utilizes a vertical gradient sensor configuration. The second is a dual frequency approach. The third makes use of dipole and quadrapole receiver configurations. The fourth looks at coils of different sizes. Each algorithm is described along with its associated sensor. Two figures of merit ultimately define algorithm/sensor performance. The first is the depth of penetration obtainable. (That is, the maximum detection depth obtainable.) This describes the performance of the method to achieve detection of deep targets. The second is the achievable statistical depth resolution. This resolution describes the precision with which depth can be estimated. In this paper depth of penetration and
statistical depth resolution are qualitatively determined for each sensor/algorithm. A scientific method is used to make these assessments. A field test was conducted using 2 lanes with emplaced UXO. The first lane contains 155 shells at increasing depths from
0" to 48". The second is more realistic containing objects of varying size. The first lane is used for algorithm training purposes, while the second is used for testing. The metal detectors used in this study are the: Geonics EM61, Geophex GEM5, Minelab STMR II, and the Vallon VMV16.
In this paper we look at the scattering of electromagnetic waves from thin wires. We propose a vehicle mounted search
radar system that rotates 360° about the vertical axis. Our wire of interest is lying on a lossy ground plane. It is generally
flat but has many bends, which gives it a vertical extent. The system is designed using a wire scattering simulator to
predict the response of a test wire to various illuminations. The simulator makes use of the Method of Moments technique
to predict the scattering of E&M waves in 3D. Several approximations make the tool fast and versatile. Among these is
the general assumption of the wire as a metal filament (with infinitesimal radius). To include a lossy ground plane we
suggest the use of the NEC2 simulator. In the development of this problem, we first look at scattering from a 3D thin
wire. The conclusion of the simulation phase of this work is that the cardinal flash or glint response of the wire must be
observed for the wire to be detectable. This response occurs when the wire is illuminated directly from the side. Because
this scenario occurs at an unknown location as the vehicle passes by the wire, our design suggests the use of a spinning
search radar. A brief experiment is performed using a search radar as a validation of concept. The observed glint response
is shown and suggestions are made for how a practical system could reduce false alarms. We conclude the paper with a
preferential configuration for a search radar suggested by simulation for this given application.
Object depth is a simple characteristic that can indicate an object's type. Popular instruments like radar, metal
detectors, and magnetometers are often used to detect the presence of a subsurface object. The next question
is often, "How deep is it?" Determining the answer, however, is not as straight forward as might be expected.
This paper explores the determination of depth using metal detectors. More specifically, it looks at a popular
metal detector (the Geonics EM61) and makes use of its vertically separated coils to generate a depth estimate.
Estimated depths are shown for UXO and small surface clutter from flush buried down to 48". Ultimately
a statistical depth resolution is determined. An alternative approach is then considered that casts the depth
determination problem as one of classification. Only two classes are considered important "deep" and "shallow".
Results are shown that illustrate the utility of the classifier approach. The traditional estimator can provide a
depth estimate of the object, but the classifier approach can distinguish between small shallow, large deep, and
large shallow object classes.
Landmine sensor technology research has proposed many types of sensors. Some of this technology has matured and can be implemented in sensor arrays that scan for landmines. Other technologies show great promise for distinguishing landmines from clutter, but are more practical to implement on a point-by-point basis as confirmation sensors. This work looks at the problem of scheduling confirmation sensors. Three sensors are considered for their ability to distinguish between landmines and clutter. A novel sensor scheduling algorithm is employed that learns an optimal policy for applying confirmation sensors based on reinforcement learning. A performance gain is realized in both probability of correct classification and processing time. The processing time savings come from not having to deploy all sensors for every situation.
See Through The Wall (STTW) radar applications have become of high importance to Homeland Security and Defense needs. In this work surface penetrating radar is simulated using basic physical principles of radar propagation and polarimetric scattering. Wavenumber migration imaging is applied to simulated radar data to produce polarimetric imagery. A detection algorithm is used to identify dihedral scattering signatures for mapping inner building walls. The detector utilizes two polarimetric channels: HH and VV to classify objects as outer wall, inner wall, or object within room. The final product is a data generated building model that maps the interior walls of the building.
A method known as active sensing is applied to the problem of landmine detection. The platform utilizes two scanning sensor arrays composed of ground penetrating radar (GPR) and electromagnetic induction (EMI) metal detectors. Six simulated confirmation sensors are then dynamically deployed according to their ability to enhance information gain. Objects of interest are divided into ten class types: three classes are for metal landmines, three classes for plastic landmines, three classes for clutter objects, and one final class for background clutter. During the initial scan mode, a uniform probability is assumed for the ten classes. The scanning measurement assigns an updated probability based on the observations of the scanning sensors. At this point a confirmation sensor is chosen to re-interrogate the object. The confirmation sensor used is the one expected to produce the maximum information gain. A measure of entropy called the Renyi divergence is applied to the class probabilities to predict the information gain for each sensor. A time monitoring extension to the approach keeps track of time, and chooses the confirmation sensor based on a combination of maximum information gain and fastest processing time. Confusion matrices are presented for the scanning sensors showing the initial classification capability. Subsequent confusion matrices show the classification performance after applying active sensing myopically and with the time monitoring extension.
A method is presented for identifying buried objects using electromagnetic induction metal detectors. The method uses a physics based model for identifying two basis functions that fundamentally compose metal detector signals. These bases form a signal subspace that contains the signals from all objects at the same depth regardless of their shape, size, or metal content. First, an algorithm for determining this subspace is presented. Then utilizing the proper signal subspace, the shape of the object is determined by estimating the object's directional polarizablity.
Landmine data for electromagnetic induction (EMI) and ground penetrating radar (GPR) sensors has been collected in two background environments. The first environment is clay and the second is gravel. A multi-modal detection algorithm that utilizes a Maximum A Posteriori (MAP) approach is applied to the clay background data and compared to a pair of similar MAP detectors that utilize only the single sensors. It is shown that the multi-modal detector is more powerful than both single mode detectors regardless of landmine type. The detectors are then applied to the data from the gravel background. It is shown that a more powerful performance is achieved if the MAP detector adapts to the statistics of the new background rather than training it a priori with broader statistics that encompass both environmental conditions.
A characteristic of vehicle-based ground-penetrating radar is the hyperbolic signature generated by targets such as landmines. The hyperbola provides a significantly different shape from most false alarms. Here an approach is introduced that seeks to utilize all of the energy contained in this characteristic hyperbolic signature. We propose a Hyperbola Flattening Transform (HFT) that transforms hyperbolic signatures of interest into straight lines, which are in turn detected using the Radon transform. The algorithm is applied to both simulated and real data. Encouraging results are presented when applying the HFT to the problem of detecting low signal-to-noise ratio plastic mines.
Ground penetrating radar (GPR) has been shown to be useful in the detection of landmines. It is of great interest to extend this capability to discrimination between landmines and other objects cluttering the battlefield environment. Wavenumber migration processing (SAR imaging) is used here to show the ability of a GPR to determine both burial depth and size of landmines. Wavenumber migration imaging is summarized and an automated algorithm for extracting size and depth is introduced. A repeatability study is presented for ten signatures from the same metallic landmine. An example of 2D wavenumber migration imaging is presented, as well as, a summary of landmine size and depth estimates from the ten signatures.
The Mine Hunter/Killer system employs a ground penetrating radar (GPR). Twenty antennas sample a 3m swath to measure a 3D depth return from the earth as the vehicle moves forward in a lane. Data has been collected on shallow and deep, metal and low metal landmines. Samples signatures from a metal and plastic cased landmines buried at 6 inches are presented. In each example a hyperbolic signature is observed. Two feature sets that exploit the hyperbolic shape for false alarm reduction are presented. The first uses a pixel clustering technique to isolate the hyperbola in 3D. A vector of size/shape features is extracted and combined with a quadratic polynomial discriminant into a single value. The second feature set utilizes the radon transform. The radon transform sums the tails of the hyperbola allowing the algorithm to differentiate between surface clutter, which tends to be oriented horizontally in depth, and the diagonals of the hyperbola. Performance curves for both the 3D size/shape features and the radon feature are presented.
A neural network is applied to data collected by the close-in detector for the Mine Hunter Killer (MHK) project with promising results. We use the ground penetrating radar (GPR) and metal detector to create three channels (two from the GPR) and train a basic, two layer (single hidden layer), feed-forward neural network. By experimenting with the number of hidden nodes and training goals, we were able to surpass the performance of the single sensors when we fused the three channels via our neural network and applied the trained net to different data. The fused sensors exceeded the best single sensor performance above 95 percent detection by providing a lower, but still high, false alarm rate. And though our three channel neural net worked best, we saw an increase in performance with fewer than three channels, as well.
In this presentation, we compare the gain in performance offered by combing the result of a ground-penetrating radar, an electromagnetic induction metal detector, and a magnetometer (MAG) against the performance offered by any one of these sensors alone on the problem of buried mine and unexploded ordnance detection. Using the community-wide DARPA background clutter data set, we characterize the single-channel performance of each of these detectors, describing the preprocessing and detection processing used for each. We then combine the sensor results, using a variety of binary decision-level Boolean methods. A performance gain was observed as a two-to-threefold reduction in the false alarm rate, operating at an 80 percent probability of detection, for 'majority voting', which was the best of the combining methods.
The MH/K) Close in Detector (CID) system employs ground penetrating radar (GPR), forward looking IR and metal detectors that are individually and collectively processed to generate automatic target detections. The detection preprocessing is initially being accomplished on a sensor by sensor basis. For the IR sensor, we apply digital filtering techniques and morphology to detect the mines and a separate filter to characterize the background level. For preprocessing the GPR returns, we apply an inverse Fourier transform to the complex frequency return signal to obtain depth information and apply digital filtering techniques to remove fixed pattern noise and provide contrast enhancement. Metal detector returns are preprocessed using a distance measure of the return compared with the averaged background. A representative data set is extracted from the preprocessed data for each of the sensor types. Other MH/K team efforts for ATR and fusion development include TRW, BAE and Sandia National Laboratories.
A feature based approach is taken to reduce the occurrence of false alarms in foliage penetrating, ultra-wideband, synthetic aperture radar data. A set of 'generic' features is defined based on target size, shape, and pixel intensity. A second set of features is defined that contains generic features combined with features based on scattering phenomenology. Each set is combined using a quadratic polynomial discriminant (QPD), and performance is characterized by generating a receiver operating characteristic (ROC) curve. Results show that the feature set containing phenomenological features improves performance against both broadside and end-on targets. Performance against end-on targets, however, is especially pronounced.
We present a low-complexity model-based FOPEN target detection algorithm and discuss its potential application as a target screener within an end-to-end FOPEN SAR automatic target detection system. The algorithm uses multiple discriminants extracted over a local sliding window followed by a multivariate discrimination rule to perform target screening at the pixel level. We present detection performance results obtained against FOPEN SAR imagery and show that the multidiscriminant approach achieves better detection performance than a model-template matched-filter detection algorithm.