Jay A. Marble

Phase Distortion Correction for See-Through-The-Wall Imaging Radar

See through the wall (STTW) applications have become of high importance to law enforcement, homeland security and defense needs. In this work surface penetrating radar is simulated using basic physical principles of radar propagation. Wavenumber migration is employed to form 2D images of objects found behind a wall. It is shown that this technique cannot properly image with the wall present because of an unknown phase delay experienced by the electromagnetic waves as they pass through the wall. Two approaches are taken to estimate this phase by looking at the direct backscatter signal from the wall. The first is a dual phase approach, which uses a non-parametric technique to find the phase at every frequency. The second method is a dual frequency approach. The two frequencies are close enough together that the reflection coefficients are approximately equal. This approximation allows for more observations than unknown parameters. The surface reflection coefficient, back wall coefficient, and phase are simultaneously determined using an iterative, non-linear (Newton-Raphson) successive approximation algorithm. Comparisons are performed for a simple scenario of three point scatterers with and without phase correction.

See through the wall detection and classification of scattering primitives

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.

Physics derived basis pursuit in buried object identification using EMI sensors

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 objects directional polarizablity.

Sensor management for landmine detection

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.

Landmine signatures in ground-penetrating radar

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.

Outdoor synthetic aperture acoustic ground target measurements

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.

Proposed design of search radar for thin wire detection

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.

Multimodal, adaptive landmine detection using EMI and GPR

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.

Adapting Physically Complete Models to Vehicle-Based EMI Array Sensor data: Data inversion and Discrimination Studies

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 objects response to a sensors 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.

Target localization techniques for vehicle-based electromagnetic induction array applications

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.