Forward looking ground penetrating radar (FLGPR) has the benefit of detecting objects at a significant standoff distance. The FLGPR signal is radiated over a large surface area and the radar signal return is often weak. Improving detection, especially for buried in road targets, while maintaining an acceptable false alarm rate remains to be a challenging task. Various kinds of features have been developed over the years to increase the FLGPR detection performance. This paper focuses on investigating the use of as many features as possible for detecting buried targets and uses the sequential feature selection technique to automatically choose the features that contribute most for improving performance. Experimental results using data collected at a government test site are presented.
This paper proposes a machine learning algorithm for subsurface object detection on multiple-input-multiple-output (MIMO) forward-looking ground-penetrating radar (FLGPR). By detecting hazards using FLGPR, standoff distances of up to tens of meters can be acquired, but this is at the degradation of performance due to high false alarm rates. The proposed system utilizes an anomaly detection prescreener to identify potential object locations. Alarm locations have multiple one-dimensional (ML) spectral features, two-dimensional (2D) spectral features, and log-Gabor statistic features extracted. The ability of these features to reduce the number of false alarms and increase the probability of detection is evaluated for both co-polarizations present in the Akela MIMO array. Classification is performed by a Support Vector Machine (SVM) with lane-based cross-validation for training and testing. Class imbalance and optimized SVM kernel parameters are considered during classifier training.
KEYWORDS: General packet radio service, LIDAR, Land mines, Detection and tracking algorithms, Antennas, Metals, Target detection, Sensors, Prototyping, Global Positioning System
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
re
ection. Under extremely rough terrain, antenna bounce and multipath eects render nding the ground
re
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.
KEYWORDS: General packet radio service, Data modeling, LIDAR, Mining, Detection and tracking algorithms, Land mines, Target detection, Antennas, Motion models, Soil science
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.
KEYWORDS: Antennas, General packet radio service, Image quality, Land mines, Image processing, Roads, Mining, Detection and tracking algorithms, Data processing, Ground penetrating radar
Ground Penetrating Radar (GPR) has been applied for several years to the problem of detecting both anti-personnel and
anti-tank landmines. One area of research is using Forward Looking GPR (FLGPR) to detect mines. While FLGPR has
the advantage of standoff versus downward looking GPR, the responses from buried targets generally decrease while the
responses from clutter increase. One source of clutter is from sidelobes and grating lobes caused by off-road clutter. As
it is not possible to get a narrow beamwidth at the low frequencies required to get ground penetration, FLGPR receives
responses from both on and off the road. Off-road clutter responses are often much stronger than the responses from
buried mines. These off-road clutter objects can produce sidelobes that overlap with and obscure the responses from inroad
targets. This becomes especially problematic if the antenna array spacing is not fine enough and grating lobes are
formed. To reduce both the sidelobes and grating lobes, a technique using L1-norm minimization was tested. One
advantage of this technique is it only requires a single aperture. The resulting image retains phase information which
allows the images to be then coherently summed, resulting in better quality images. In this paper a description of the
algorithm is provided. The algorithm was applied to a FLGPR data set to show its ability to reduce both sidelobes and
grating lobes. Resulting images are shown.
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[1]. 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.
Researchers in academia have successfully demonstrated acoustic landmine detection techniques. These typically employ acoustic or seismic sources to induce vibration in the mine/soil system, and use vibration sensors such as laser vibrometers or geophones to measure the resultant surface motion. These techniques exploit the unique mechanical properties of landmines to discriminate the vibration response of a buried mine from an off-target measurement. The Army requires the ability to rapidly and reliably scan an area for landmines and is developing a mobile platform at NVESD to meet this requirement. The platform represents an initial step toward the implementation of acoustic mine detection technology on a representative field vehicle. The effort relies heavily on the acoustic mine detection cart system developed by researchers at the University of Mississippi and Planning Systems, Inc. The NVESD platform consists of a John Deere E-gator configured with a robotic control system to accurately position the vehicle. In its present design, the E-gator has been outfitted with an array of laser vibrometers and a bank of loudspeakers. Care has been taken to ensure that the vehicle’s mounting hardware and data acquisition algorithms are sufficiently robust to accommodate the implementation of other sensor modalities. A thorough discussion of the mobile platform from its inception to its present configuration will be provided. Specific topics to be addressed include the vehicle’s control and data acquisition systems. Preliminary results from acoustic mine detection experiments will also be presented.
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: Resistive Vee , Antipodal Vivaldi, and Planning Systems Inc's (PSI) Archimedean Spiral 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.
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