Vehicle-mounted ground-penetrating radar (GPR) has proved to be a valuable technology for buried threat
detection, especially in the area of military route clearance. However, detection performance may be degraded in
very rough terrain or o-road conditions. This is because the signal processing approaches for target detection
in GPR rst identify the ground re
ection in the data, and then align the data in order to remove the ground
ection. Under extremely rough terrain, antenna bounce and multipath eects render nding the ground
ection a dicult task, and errors in ground localization can lead to data alignment that distorts potential
target signatures and/or creates false alarms. In this work, commercial-o-the-shelf light detection and ranging
(LIDAR), global positioning system (GPS), and inertial measurement unit (IMU) were integrated with a GPR
into a prototype route clearance system. The LIDAR provided high-resolution measurements of the ground
surface prole, and the GPS/IMU recorded the vehicle's position and orientation. Experiments investigated
the applicability of the integrated system for nding the ground re
ection in GPR data and decoupling vehicle
motion from the rough surface response. Assessment of ground-tracking performance was based on an experiment
involving three prepared test lanes, each with dierent congurations of buried targets and terrain obstacles.
Several algorithms for target detection in GPR were applied to the data, both with traditional preprocessing and
incorporating the LIDAR and IMU. Experimental results suggest that the LIDAR and IMU may be valuable
components for ground tracking in next-generation GPR systems.
Ground Penetrating Radar (GPR) has been applied for several years to the problem of detecting both anti-personnel and
anti-tank landmines. One major challenge for reliable mine detection using GPR is removing the response from the
ground. When the ground is flat this is a straightforward process. For the NIITEK GPR, the flat ground will show up as
one of the largest responses and will be consistent across all the channels, making the surface simple to detect and
remove. Typically, the largest responses from each channel, assumed to be the surface, are aligned in range and then
zeroed out. When the ground is not flat, the response from the ground becomes more complicated making it no longer
possible to just assume the largest response is from the ground. Also, certain soil surface features can create responses
that look very similar to those of mines. To further complicate the ground removal process, the motion of the GPR
antenna is not measured, making it impossible to determine if the ground or antenna is moving from just the GPR data.
To address surface clutter issues arising from uneven ground, NVESD investigated profiling the soil surface with a
LIDAR. The motion of both the LIDAR and GPR was tracked so the relative locations could be determined. Using the
LIDAR soil surface profile, GPR data was modeled using a simplified version of the Physical Optics model. This
modeled data could then be subtracted from the measured GPR data, leaving the response without the soil surface.
In this paper we present a description and results from an experiment conducted with a NIITEK GPR and LIDAR over
surface features and buried landmines. A description of the model used to generate the GPR response from the soil and
the algorithm that was used to subtract the two provided. Mine detection performances using both GPR only and GPR
with LIDAR algorithms are compared.
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 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 <i>depth of penetration</i> 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 <i>statistical depth resolution</i>. This resolution describes the precision with which depth can be estimated. In this paper <i>depth of penetration</i> and
<i>statistical depth resolution </i>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.
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.
Electromagnetic induction (EMI) sensors and magnetometers have successfully detected surface laid, buried, and visually obscured metallic objects. Potential military activities could require detection of these objects at some distance from a moving vehicle in the presence of metallic clutter. Results show that existing EMI sensors have limited range capabilities and suffer from false alarms due to clutter. This paper presents results of an investigation of an EMI sensor designed for detecting large metallic objects on a moving platform in a high clutter environment. The sensor was developed by the U.S. Army RDECOM CERDEC NVESD in conjunction with the Johns Hopkins University Applied Physics Laboratory.
This paper describes the Multi-mode Electromagnetic Target Discriminator (METD) sensor and presents preliminary results from recent field experiments. The METD sensor was developed for the US Army RDECOM NVESD by The Johns Hopkins University Applied Physics Laboratory. The METD, based on the technology of the previously developed Electromagnetic Target Discriminator (ETD), is a spatial scanning electromagnetic induction (EMI) sensor that uses both the time-domain (TD) and the frequency-domain (FD) for target detection and classification. Data is collected with a custom data acquisition system and wirelessly transmitted to a base computer. We show that the METD has a high signal-to-noise ratio (SNR), the ability to detect voids created by plastic anti-tank (AT) mines, and is practical for near real-time data processing.
Current state-of-the-art electromagnetic induction (EMI) metal detector research systems have shown the potential to detect low metal content buried targets as well as discriminate the type of target as a mine or clutter. However, further research is needed to investigate metal target discrimination potential for closely spaced metal targets. A series of experiments designed to investigate the spatial and time decay responses of multiple metal targets were conducted using a spatial scanning, time-domain EMI metal detector. Time decay signatures were taken of two calibration targets placed over varying distances with the objective of analyzing target identification and spatial resolution. This paper presents results of these experiments.