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.
Ground Penetrating Radar (GPR) has been applied for several years to the problem of detecting both antipersonnel
and anti-tank landmines. RDECOM CERDEC NVESD is developing an airborne wideband
GPR sensor for the detection of minefields including surface and buried mines. In this paper, we describe
the as-built system, data and image processing techniques to generate imagery, and current issues with
this type of radar. Further, we will display images from a recent field test.
Ground penetrating radar (GPR) is emerging as viable technology for rapid and accurate landmine detection. Although GPR has been successfully used for landmine and subsurface object detection, the performance of GPR is dependent on the type of medium the subsurface object is buried in. In a previous paper, we compared the imaging response of two antennas in three soils to steel spheres. In this paper, we compare the imaging response of spheres of different materials in different soils and compute energy levels for three regions of interest in the images.
Ground Penetrating Radar (GPR) has been applied for several years to the problem of detecting both anti-personnel and anti-tank landmines. Most of the evaluation effort has focused on obtaining the end-to-end performance metrics (e.g. Pd and pfa ) of complete detection systems. This is the fourth in a series of papers in which we focus on the specific performance of one critical component of GPR systems: the antenna subsystem. In this paper, we examine several free-space characteristics of 3 prototype wideband antennas, here denoted by the terms: <i>Resistive Vee , Antipodal Vivaldi</i>, and Planning Systems Inc's (PSI) <i>Archimedean Spiral </i>antennas. Specifically, we (1) determine gain and phase properties of these antennas, (2) measure the internal reflections, (3) determine the direct coupling between antennas used in bistatic pairs, (4) measure antenna reflectivity, and (5) measure the spatial response footprints.
Ground Penetrating Radar has been applied for several years to the problem of detecting both anti-personnel and anti-tank landmines. Most of the evaluation effort has focused on obtaining the end-to-end performance metrics (e.g. Pd and pfa ) of complete detection systems. This is the third in a series of papers in which we focus on the specific performance of one critical component of GPR systems: the antenna subsystem. In this paper, we examine several free-space characteristics of Planning Systems Inc. Archimedean Spiral Antennas. Specifically, we (1) investigate a spurious signal response observed with a large metal plate reflecting target, (2) determine gain and phase properties of these antennas, (3) calculate the antennas' impulse response, and (4) image several targets to validate our approach.