Down-looking ground penetrating radar (DLGPR) has been used extensively for buried target detection. Performance of a DLGPR is typically measured by calculating the probability of detection (PD) and the false alarm rate (FAR) against a target set in a particular soil type. Variability in target sets, including target construction, size, layout, and burial depth, make comparing performance of a DLGPR across test sites and soil compositions a challenge. This paper describes a recent effort to collect data against a standardized set of target types, layouts, and depths. The goal of this effort is to have data sets collected in a uniform manner at various test sites in Australia and Canada for more meaningful comparisons of DLGPR performance in a range of soil types. The data is to be used to improve algorithms for the automatic detection of targets. This paper will describe test planning and execution, and discuss high-level DLGPR results and ongoing analyses from the Australian data collection.
The U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) Countermine Division has developed a program to investigate multiple sensor modalities to detect side-attack explosive hazards. Sensor modalities include vehicle-mounted forward- and side-looking radar, side-looking acoustic, and forward- and sidelooking electro-optical/infrared sensors. NVESD is collaborating with the Institute for Defense Analyses and multiple universities to execute data collections and conduct data analysis and algorithm development. A variety of sensors have been tested at a U.S. Army test site, and performance has been measured by calculating the probability of detection and the false-alarm rate for a given target set. Reliable detection is challenging due to variations in target design, target emplacement, environment, and obscuration. Current analysis is focused on developing feature-extraction methods and determining sensors’ abilities to penetrate concealment. This paper will discuss several data analysis efforts to date that have resulted in consideration of a high-frequency 3D radar for detection and discrimination.
KEYWORDS: Target detection, General packet radio service, Detection and tracking algorithms, Ground penetrating radar, 3D acquisition, Global Positioning System, Automatic target recognition, Sensors, Radar, Data processing
Down-looking ground-penetrating radar (DLGPR) has been used extensively for buried target detection. For operational implementations, the sensor is used in direct-detection mode, where algorithms process data while the system moves down roadways. Decisions are made before a system passes over the target. Change detection works by passing over an area before and after targets are buried. By comparing before-and-after data, change detection can improve DLGPR performance, but it also has inherent operational limitations. Performance enhancements include mitigating the effects of anomalies not associated with targets and increasing the detection probabilities of deeper targets through indirect means. In the latter case, deeply buried targets that do not appear in the GPR data can be indirectly detected using change detection methods if the patch of ground where the target is buried has been significantly modified from its original undisturbed state. In this paper, we explore decision-based change-detection approaches for enhancing the performance of a DLGPR system and enumerate the limitations of the approach.
Field experience has shown that soil conditions can have large effects on the ability of ground-penetrating radar (GPR) to detect buried targets of interest. The relative permittivity of the soil determines the attenuation of the radar signal. The contrast between the relative permittivity of the soil and the target is critical to determining the strength of the reflection from the target. In this paper, to measure the relative permittivity of the soil and various target fill materials, a microstrip ring resonator is placed in contact with a material medium. The real and imaginary parts of the relative permittivity are determined from (1) changes in resonant frequencies (between 600 MHz and 2 GHz) and (2) the quality factor of the resonator, respectively. Measurement results are compared to data collected by a GPR.
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.
For vehicle-mounted down-looking ground penetrating radar (DLGPR) systems, the largest response is typically due
to the radar reflecting off the ground. Most DLGPR algorithms remove the ground bounce response as a first preprocessing
step. The remaining subsurface response is then used to detect buried mines. It was observed that the
ground bounce response over recently buried mines differs from the surrounding undisturbed soil. This suggests an
approach in which the ground bounce response could be used to enhance detection performance. In this paper, we
describe a technique for fusing the GPR ground bounce response with the GPS subsurface response to enhance mine
detection performance. The technique is applied to data collected by a wide bandwidth impulse radar over buried
mines in various soil conditions.
KEYWORDS: Land mines, Autoregressive models, General packet radio service, Digital filtering, Genetic algorithms, Data modeling, Signal processing, Electronic filtering, Detection and tracking algorithms, Optimization (mathematics)
Previous large-scale blind tests of anti-tank landmine detection utilizing the NIITEK ground penetrating radar indicated the potential for very high anti-tank landmine detection probabilities at very low false alarm rates for algorithms based on adaptive background cancellation schemes. Recent data collections under more heterogeneous multi-layered road-scenarios seem to indicate that although adaptive solutions to background cancellation are effective, the adaptive solutions to background cancellation under different road conditions can differ significantly, and misapplication of these adaptive solutions can reduce landmine detection performance in terms of PD/FAR. In this work we present a framework for the constrained optimization of background-estimation
filters that specifically seeks to optimize PD/FAR performance as measured by the area under the ROC curve between two FARs. We also consider the application of genetic algorithms to the problem of filter optimization for landmine detection. Results indicate robust results for both static and adaptive background cancellation schemes, and possible real-world advantages and disadvantages of static and adaptive approaches are discussed.
KEYWORDS: Sensors, Target detection, Land mines, Ground penetrating radar, Radar, General packet radio service, Signal detection, Detection and tracking algorithms, Target recognition, Data modeling
In this work we present an application of matched subspace detectors to the problem of target detection and identification using ground penetrating radar data. In particular we apply sets of matched subspace detector filter banks to data containing both anti-personnel and anti-tank targets as well as metallic and non-metallic clutter objects. Current results indicate the potential for robust target detection and identification but further improvements via subspace modeling and signal extraction/enhancement may also improve performance.
KEYWORDS: Land mines, Antennas, General packet radio service, Mining, Radar, Detection and tracking algorithms, Automatic target recognition, Sensors, Ground penetrating radar, Software development
Down-looking ground penetrating radar (DLGPR) has been used extensively for landmine detection. Most operational prototype systems and data collection devices use multiple transmit and receive antennas that are directed downward and mounted at the front or bottom of a moving platform. The resultant 3D datasets generated by these devices have commonalities that lend themselves to systematic analysis. While in-air measurements allow for GPR antenna characterization, it has been difficult to compare the effectiveness of a specific GPR antenna configuration as applied to the landmine detection problem. We have developed software and analysis techniques to bridge the gaps in understanding that exist between GPR antenna characterization and assessment of DLGPR landmine detection systems. We examine several datasets that were collected over an identical set of buried landmines by different DLGPR systems, and compare the mine signatures by using a simple measure of effectiveness. The simplicity of the metric allows one to separate the effects of the system from the algorithms designed to enhance mine detection performance.
KEYWORDS: Mining, Land mines, Synthetic aperture radar, Sensors, Radar, General packet radio service, Global Positioning System, Antennas, Calibration, Target detection
In the last few decades, the Army has developed and tested vehicular platforms for detecting landmines in roadways. These platforms include ground penetrating radar (GPR), infrared (IR) cameras, electromagnetic induction (EMI) sensors, or some combination of the three. Typically, the sensors are mounted at the front of the vehicle and are directed downward. Detecting surface laid and buried landmines at standoff require that the sensors be forward-looking. Issues of critical importance to the testing and evaluation of forward-looking sensors include geo-location and scoring. We present here detailed descriptions of tests designed to evaluate forward-looking GPR sensors used for landmine detection. We find that careful test design and analysis is necessary to accurately assess the performance of forward-looking GPR as applied to mine detection.
In this paper we present a multi-stage algorithm for target/clutter discrimination and target identification using the Niitek/Wichmann ground penetrating radar (GPR). To identify small subsets of GPR data for feature-processing, a pre-screening algorithm based on the 2-D lattice least mean squares (LMS) algorithm is used to flag locations of interest. Features of the measured GPR data at these flagged locations are then generated and pattern recognition techniques are used to identify targets using these feature sets. It has been observed that trained human subjects are often quite successful at discriminating targets from clutter. Some features are designed to take advantage of the visual aberrations that a human observer might use. Other features based on a variety of image and signal processing techniques are also considered. Results presented indicate improvements for feature-based processors over pre-screener algorithms.
Reducing the false alarm rate of vehicular and hand-held mine detection systems has been a goal of most countermine detection programs. No thorough investigation into the causes of false alarms has been conducted to date. We present here an investigation into the sources of persistent ground-penetrating radar (GPR) false alarms that occurred during testing of a vehicular mine detection system. Data collected with this system was used to identify false alarms that persisted over several tests conducted over a two-year period over the same simulated roadway. A dig list was generated and several sites were excavated. Soil samples were collected at the sites and analyzed in the lab. The results of the excavation will be presented.
KEYWORDS: Mining, Sensors, Land mines, Laser Doppler velocimetry, Data processing, Microwave radiation, Palladium, Antennas, Radar, General packet radio service
Assessing the performance of mine-detection systems usually means calculating probability of detection (Pd and a false-alarm rate (FAR). relying on these measures of performance is a consequence of the way in which mine detection systems are tested. Most advanced technology demonstrations of mine detection systems require the participating contractors to provide the testing agency with a set of alarms, or declarations, that correspond to locations on the ground where a mine is suspected to be buried. Superimposing these alarms with the ground truth, or baseline, allows one to compute the Pd and the FAR, but does not give insight into issues such as signal-to-noise ratios or signal-to-clutter ratios. With knowledge of S/N and S/C ratios, expected performance can be compared with demonstrated performance to determine how sensor sensitivity affects overall performance. In addition, S/C ratios provide a means to judge relative performance, but Pd and FAR alone can be ambiguous.
To date, most of the vehicular-mounted mine detection systems employing ground-penetrating radar are down looking in the sense that the array of radar antennas is approximately 1-m forward of the vehicle and pointed straight down. Advantages of systems that are able to look forward of the vehicle by more than 10 m include the ability to make detections at greater stand-off distances and to use mulitpe looks at targets to discriminate mines from clutter. Data collected by Jaycor's forward-looking ground- penetrating radar (FLGPR) system provides a means by which these advantages can be assessed. In February 1999, Jaycor took, its FLGPR to the antitank (AT) mine lanes at Socorro, New Mexico. Jaycor made several excursions over simulated roads that contained a mix of metal- and plastic-cased AT mines on the surface and buried up to 4 in.
In March of 1999, a research team from the University of Mississippi brought its data acquisition system consisting of an acoustic/seismic laser Doppler vibrometer (LDV) mine detection sensor, to Fort A P Hill in Virginia. The purpose was to collect data over a variety of miens and to participate in a blind test. IN the blind test, the mine detection apparatus was brought to several 1-m by 1-m areas included a mix of mines, blank spots., and clutter spots as determined from prior test. The data collected over each of these spots was visualized in real time, an a mine/no mine decision was made. The resultant probability of detection was 95 percent with a false-alarm rate (FAR) of 0.03 m-3. We present a description of the test and a detailed analysis of the data collected by the University of Mississippi in the mine lanes at AP Hill. With knowledge of the baseline, we compute target and clutter statistics, including signal-to-clutter ratios for various categories of mine types and mine depths. We examine detection trends as a function of frequency. Applying image-processing techniques to the data, features such as size and shape are extracted, and the resultant feature-level target and clutter histograms are used to improve performance. The expected performance with a without feature is compared to the demonstrated performance.
Passive IR sensors have been demonstrated to be effective for detecting surface landmines. For shallow buried landmines, detection rates and false-alarms rates are poorer and highly dependent on environmental conditions such as time of day and cloud cover. An advantage touted by advocates of passive microwave sensors is that their performance does not depend on the time of day and the inherent soil-mine temperature differences. To assess the complementary detection potential of passive microwave sensors, data was collected on a Thomson-Thorne microwave sensor at a test site in England. This sensor was mounted on a scanning rack apparatus and operated at a frequency of 10 Ghz. The test included investigations of both antitank and antipersonnel miens at the surface and to depths of 2 inches. Analysis of the raw data shows that surface and buried targets produce signals that are significantly higher than background clutter. In this paper, we present a brief description of the passive-microwave detection apparatus and the data-collection exercises that were completed. Analysis of the raw sensor data is then presented with an emphasis on comparing signal strengths of mines with signals reflected from the soil in the absence of mines. Particular attention is paid to the effects of varied incident sensor angles, sensor polarizations, and system scan speeds on sensor performance. Signal-to-noise as well as signal-to-clutter ratios are calculated as a function of these different variables.
Clutter is the largest factor contributing to the poor detection rates and high false-alarm rates for mine and unexploded ordnance (UXO) detection systems. The source of this clutter can be either naturally occurring or anthropic. Because the standard detector technologies are anomaly-based systems, few features within the sensor data permit mitigation of false alarms or provide an avenue to enhance detection rates. To achieve operational detection performance, a better understanding of clutter statistics is required at the single pixel level and at the feature level. This paper presents an in-depth assessment of the statistical properties of clutter and target signatures for a specific test site. This assessment uses data collected during the Defense Advanced Research Projects Agency (DARPA) Background Clutter Data Collection Experiment. Pixel-level statistics for electromagnetic induction detection systems are discussed. The resulting statistical distribution functions for clutter and targets exhibit poor separation. Improved separation of the distribution functions is achieved if features are employed. For example, by measuring the particular size and shape features of target signatures, the false-alarm rate can be reduced with minimal decrease in the detection rate. By using feature-level information, improved system performance can be achieved. This improved performance is dependent on the feature-level statistics of a specific site and is always limited by the overlap between the distribution functions of the clutter and target signatures. The resulting performance enhancement -- although significant -- is still far below the level required for very high detection rates and low false- alarm rates.
KEYWORDS: Sensors, Magnetometers, Land mines, Metals, Magnetic sensors, Magnetism, Electromagnetic coupling, Mining, Infrared sensors, General packet radio service
Most technologies in use or proposed for use to detect landmines and unexploded ordnance (UXO) suffer from unacceptably high false-alarm rates, even at modest probabilities of detection. High false-alarm rates are a consequence of the inability to discriminate real UXO and landmines from man-made and naturally occurring clutter. The goal of the Defense Advanced Research Project Agency (DARPA)- sponsored Background Clutter Data Collection Experiment is to provide data which will support the development of techniques that are more adept at discriminating UXO from benign, man- made objects. During the fall of 1996, high areal density site surveys were completed using the following sensor types: magnetometer, infrared, electromagnetic induction, and ground- penetrating radar. Preliminary analysis of the data confirmed that a large number of anomalies in the sensor data are visually indistinguishable from anomalies that are a result of emplaced inert UXO or landmines. The Firing Point 20 site at Fort A. P. Hill exhibits the largest number of these ordnance- like anomalies. To determine the source of a subset of these sensor response anomalies, a 1-week excavation effort was conducted. This paper presents an analysis of the data to determine the candidate locations for, the procedures used during, and the results of the excavation.
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