When using non-ground coupled Ground Penetrating Radar (GPR) to detect buried objects, one major source of clutter is the surface of the ground. This is especially true if the surface of the ground is not very flat. Responses from the surface will show up below the main response from the ground and mix with the responses of buried objects. To better understand this response and potentially remove it I investigated measuring the soil surface profile with a LIDAR and then modeling the GPR response using the FDTD code gprMax. The modeled response was then subtracted from the measured GPR data.

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.

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.

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 off-road conditions. This is because the signal processing approaches for target detection in GPR first identify the ground reflection in the data, and then align the data in order to remove the ground reflection. Under extremely rough terrain, antenna bounce and multipath effects render finding the ground reflection a difficult 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-off-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 profile, and the GPS/IMU recorded the vehicle's position and orientation. Experiments investigated the applicability of the integrated system for finding the ground reflection 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 different configurations 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 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 anti-personnel 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 land mine and subsurface object detection, the performance of GPR is affected by surface roughness of the soil medium. There have been theoretical studies showing that surface roughness degrades GPR performance, but few empirical studies have been performed. In this paper, we report on an empirical study of the effect of rough, flattened, and smooth surfaces on GPR images of an easily understood target.

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.(1) 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.

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 dependant on the type of medium the subsurface object is buried in. In this paper, we report studies of the effect of three different soils. on two sets of GPR antenna's imaging response to an easily understood target.