Most of the research on developing automatic target recognition (ATR) algorithms for acoustic-seismic landmine detection platforms has been focused on using geometric features, such as size and shape, of anomaly to distinguish between mines and clutter. This approach has achieved some success especially in detecting larger anti-tank mines. However, for smaller anti-personnel landmines, the difference in geometric features between mines and clutter can be very small, if any. To improve the detection vs. false alarm rates, it is necessary to incorporate other features into the ATR process. It has been observed from the collected acoustic data that areas with buried mines reveal more complicated surface vibration structures, such as the ring-like pattern, at certain frequencies than what a one-dimensional lumped mass-spring-dashpot model can describe. In this paper, we utilize the distributed mine/soil interaction model developed by the University of Mississippi to describe the surface vibration patterns. We develop a modified Hankel transform to extract features from areas under interrogation. Under such transform, concentration of energy is closely related to an object's physical properties. The frequency at which the energy concentration occurs corresponds to the object's natural frequency, while the corresponding Bessel basis captures its mode shape. After de-noising the transformed data, we use the frequencies, Bessel bases, and magnitudes of the energy concentrations, together with other geometric features, to form the feature vectors. We tested these features on a dataset consisting of anti-tank and anti-personnel mines as well as blank areas and metallic and non-metallic clutter. Classifiers designed based on the combined geometric and model-based features perform significantly better than those based on the geometric features alone.
The energies of gamma rays produced by either fast or captured neutrons are unique to each element. The elements in the explosive of a buried mine are carbon, nitrogen, oxygen, and hydrogen. This paper analyzes data taken on buried explosive simulants. The gamma detector was a high purity germanium crystal with excellent energy resolution and the neutrons were produced by a compact deuterium-tritium accelerator, which generated 14 MeV neutrons. The gamma rays from the explosive must be separated from the large soil background. The utility for explosive detection of each element is separately analyzed in detail. Issues of normalization and gain shifts are also addressed.
In order to detect anti-tank mines at standoff distances, we have developed a forward-looking synthetic aperture ground penetrating radar (FLGPR). The system operates over the frequency band 766~MHz to 3.8~GHz. Our FLGPR system uses a Mill's cross transmit-receive array configuration. The receive array contains 46 Archimedean spiral antennas spaced across a 3.43 meter horizontakl aperture. The transmit
aperture can be configured to contain up to 15 transmitters in one
of two vertical configuations. Data is acquired as the system continuously moves forward at a speed of 2 to 8 kph. Synthetic aperture nearfield beamforming, a form of multi-look processing, is used to reduce clutter and produce significantly improved images of buried targets. Testa against actual buried mines on U.S. Army mine lanes indicate that the system can detect buried metallic and plastic anti tank mines. Images and analysis of data including blind test results are presented.
This paper answers in the affirmative the question: will it ever be feasible to predict useful infrared buried mine detection performance? The infrared (IR) is essentially blind at certain hours, but can have excellent vision at other times. The trick to making the IR a tactically useful tool is to plan mine detection operations during its best time of utility. Rather than use thermal models with their difficulty in representing IR imagery, we used a matched filter detector on IR video, in combination with prediction techniques using neural nets and weather data, to show that weather conditions can be successful in predicting IR mine detection performance. Prediction using mine detection models and weather data, correlated using neural nets and then predicted using weather data alone is not only theoretically feasible, but is also practical. Feasibility was demonstrated in Train A/Test A mode, where the neural nets achieved 100% prediction accuracy for both AP and AT mines. Practicality was demonstrated using single day Train A/Test B results, where 98% to 88% accuracy was achieved for AT mines from 2.5 to 12.5 hours forward, respectively. The technique is expected to be limited only by the accuracy of the short-term weather forecast.
In order to detect buried land mines in clutter, Planning Systems Incorporated has adapted its Ground Penetrating Synthetic Aperture Radar (GPSAR) technology for forward-looking applications. The Forward Looking GPSAR (FLGPSAR), is a wide-band stepped-frequency radar operating over frequencies from 400 MHz to 4 GHz. The FLGPSAR system is based on a modified John Deere E-Gator turf vehicle that is capable of remote control. Custom Archimedean spiral antennas are used to populate the GPSAR array. These antennas are designed and built by PSI and have exceptional broad-band radiation characteristics. The FLGSPAR system has been used to detect plastic and metallic landmines at U.S. Army test facilities and at PSI's engineering center in Long Beach Mississippi. Multi-look SAR processing has been shown to significantly improve the quality of FLGPSAR imagery.
KEYWORDS: Mining, Land mines, Acoustics, Velocity measurements, Laser Doppler velocimetry, Systems modeling, Signal detection, Doppler effect, System identification, Target detection
The acoustic-seismic mine detection concept is based on the principle that an area with a buried object shows different dynamic response to acoustic excitation from that of soil. In this paper, we attempt to model and identify the dynamic behavior of a landmine under acoustic excitation for the purpose of automatic mine detection. A linear distributed model is used to model the two-dimensional vibration patterns of landmines. According to modal analysis of the model, it is shown that locations of the poles remain invariant throughout the area where a mine is buried underneath, and can be used as important features for distinguishing mines from clutter. A time-domain method that utilizes the acoustic pressure measured by a microphone as the input and the ground velocity measured by a laser Doppler vibrometer (LDV) as the output was employed to identify the model parameters including the poles. Based on the invariant property of the poles, the identified poles from neighboring measurements were combined to separate any area that show features in the spatial-spectral domain that correspond to presence of a mine.
An effort is underway to develop a fused sensor system for effectively detecting both metallic and non-metallic landmines. This advanced research effort will meld two orthogonal technologies, acoustic-to-seismic coupling and ground penetrating synthetic aperture radar, into a single system with a higher probability of detection and lower false alarm rate than either technology can achieve individually. Previous testing has demonstrated that these two technologies have individually high probabilities of detection and low false alarm rates but exploit disparate phenomena to locate mines. The fact that they both produce similar data makes a high confidence mine/no mine decision possible. Future plans include a stepped development process to build a close-in detector and leveraging that experience to develop a forward-looking system capable of meeting long- term Army requirements.
KEYWORDS: Mining, Land mines, Acoustics, Solids, Laser Doppler velocimetry, Velocity measurements, Systems modeling, Night vision, Sensors, Signal detection
Acoustic-seismic coupling mine detection offers an alternative approach to distinguishing mines from clutter. The approach is based on the principle that an area with a buried object shows a different response to acoustic excitation from that of the surrounding soil. Prior research shows that the response in the low frequency range can be captured using simple physically based models under certain conditions. According to the models, areas with buried mines exhibit natural frequencies that can be determined from mine types and buried depths. In this paper, we argue that not only are the natural frequencies useful for the purpose of mine detection, but the locations of the transmission zeros are important as well. Under certain conditions, the locations of the transmission zeros are also less sensitive to changes in physical properties of mines. We take advantage of this characteristic and offer a method to improve signal-to-clutter ratio for the purpose of automatic mine detection.
KEYWORDS: Antennas, Radar, Land mines, Mining, Synthetic aperture radar, Data acquisition, Switches, Signal to noise ratio, Oscillators, Target detection
In order to detect buried land mines in clutter, Planning Systems Incorporated has developed a Ground Penetrating Synthetic Aperture Radar (GPSAR) system for the U.S. Army CECOM Night Vision and Electronic Sensors Directorate. The GPSAR system is a wide-band stepped-frequency radar operating over frequencies from 500 MHz to 4 GHz. Our GPSAR uses multiple transmit and receive antennas to acquire data at 58 across-track locations separated by 1.47 inches. Along-track data sampling is provided by the forward motion of the system. Multiple radar channels and high-speed radio frequency switching are used to accelerate the data acquisition process and increase the system's maximum speed of advance. Synthetic aperture, near-field beamforming techniques are used to reduce clutter and enhance the signature of buried objects. While the system is designed for mine detection it is capable of locating deeper objects such as buried utility pipes. Tests conducted in December 2001 at U.S. Army facilities indicate that the system can detect both metallic and plastic landmines at depths up to 6 inches. A description of the PSI GPSAR system and test results are presented.
KEYWORDS: Land mines, Sensors, Mining, Data modeling, Detection and tracking algorithms, General packet radio service, Principal component analysis, Image segmentation, Stochastic processes, Autoregressive models
A major difficulty in automatic mine detection arises from the fact that the physical properties of background soil can vary significantly from one location to another. This in turns alters the sensor signals of the buried mines. Hence, a robust ATR algorithm for mine detection requires that the algorithm be adaptable to environmental changes. Moreover, mine features used for detection should be invariant to background variation. We have developed an ATR algorithm that uses only background soil data during the training phase and mine features that are less affected by soil changes. Since the algorithm uses only the background data for training, not only is it much easier to tailor the algorithm to a minefield but the algorithm can also be adapted in real-time during operation. This further improves robustness of the process. The algorithm demonstrated good performance when tested on ground penetrating radar data acquired from U.S. Army test lanes.
KEYWORDS: Sensors, Mining, Algorithm development, Detection and tracking algorithms, Signal processing, Target detection, Land mines, Image sensors, Laser Doppler velocimetry, General packet radio service
Current minefield detection research indicates that operationally no single sensor technology will likely be capable of detecting mines/minefields in a real-time manner and at a performance level suitable for a forward maneuver unit. Minefield detection involves a particularly wide range of operating scenarios and environmental conditions, which requires deployment of complementary sensor suites. We have focused, therefore, on the development of a computationally efficient and robust detection algorithm that exploits robust image processing techniques centered on meaningful target feature sets applicable to a variety of imaging sensors. This paper presents the detection technique, emphasizing its robust architecture, and provides performance results for image data generated by complementary sensors. The paper also briefly discusses the application of this detector as a component of fusion architectures for processing returns form diverse imaging sensors, including multi-channel image data from disparate sensors.
Data fusion from two separate and orthogonal mine detection sensors developed independently by the University of Mississippi and Planning Systems Inc. has been performed. The University of Mississippi's acoustic/seismic coupling detection is based on the measurement of ground surface vibration velocity by means of acoustic excitation and a laser Doppler vibrometer. Differences in absolute surface vibration velocity, caused by the present of buried mines, are used to infer the presence of buried land mines. Planning Systems Inc. uses ground-penetrating, synthetic- aperture radar to detect subsurface electromagnetic anomalies. Detection with the GPSAR sensor is based on differences in the dielectric constant of the ground medium and that of a buried land mine. The spatial resolutions of the two measurements are similar and the two sensors measure completely different physical properties. Dat form each system are described in detail and independent examples of performance are presented. A common geo-spatial grid is defined for both sensor systems given their respective resolving capability. Methods of simultaneous display are presented and situations in which the two systems are complementary are identified.
Recent research sponsored by the Army, Navy and DARPA has significantly advanced the sensor technologies for mine detection. Several innovative sensor systems have been developed and prototypes were built to investigate their performance in practice. Most of the research has been focused on hardware design. However, in order for the systems to be in wide use instead of in limited use by a small group of well-trained experts, an automatic process for mine detection is needed to make the final decision process on mine vs. no mine easier and more straightforward. In this paper, we describe an automatic mine detection process consisting of three stage, (1) signal enhancement, (2) pixel-level mine detection, and (3) object-level mine detection. The final output of the system is a confidence measure that quantifies the presence of a mine. The resulting system was applied to real data collected using radar and acoustic technologies.
KEYWORDS: Antennas, Radar, Mining, Land mines, Synthetic aperture radar, Fourier transforms, Data acquisition, Digital signal processing, Switches, Data processing
In order to detect anti-tank mines in noisy backgrounds, we have developed a ground penetrating SAR. The system operates over the frequency band 500 MHz to 1.8 GHZ. Our GPSAR system uses multiple transmit and receive antennas to acquire stepped-frequency data at 26 cross-track focal locations each separated by 1.38 inches. System motion is used to achieve along track data sampling. Multiple radar channels and high-speed radio frequency switching techniques are used to accelerate the data acquisition process, thereby increasing the system scan rate. Synthetic aperture, nearfield beamforming techniques are used to reduce clutter. The system is optimized for mine detection but is also capable of detecting deeper objects. Test against actual miens on US Army mine lanes indicate that the system can detect both plastic and metallic anti tank mines as well as anti-personnel mines. Images and analysis of data from these test are presented.
A recent blind test and two data collections at the US Army mien test lanes at Ft AP Hill have demonstrated the great potential for the use of acoustic technology to detect buried land mines. The acoustic system built by the University of Mississippi under a contract with the Night Vision and Electronic Sensors Directorate demonstrated a very high probability of detection, a very low false alarm rate, extremely good location accuracy, and significant standoff potential. A large number of papers are being presented at this conference that deal with various specific aspects of this program. This paper will present a broad but technical overview of this program. We will describe the capabilities of this approach and the areas in which improvements are being addressed. We will discuss briefly fusion with additional sensors, which will illustrate the manner in which acoustic technology can be integrate with other sensor to form a viable and robust mine detection system. We will present the present Army requirements and operational concepts that would meet these requirements.
KEYWORDS: Antennas, Mining, Radar, Ground penetrating radar, Digital signal processing, Land mines, Photography, Switches, Data acquisition, Data processing
In order to separate buried land mines from clutter a multi- channel stepped-frequency ground penetrating radar has been developed. The system operates over the frequency band 800 MHz to 2.0 GHz. The radar incorporates advanced digital signal processing and radio frequency integrated circuit components. It uses an all-digital modulator coupled with a coherent digital quadrature receiver for making precise magnitude and phase measurements. The control interface to the radar consists of an Ethernet TCP/IP link. A parallel bank of transmit-receive antennas is used to achieve cross track sampling. System motion is used to achieve along track data sampling. Synthetic aperture near field beamforming techniques are used to image buried objects. The system is designed to detect shallowly buried metallic and non- metallic mines. A system overview is presented and result from data collection exercises are included. Images and analysis of data from a mine lane is presented.
KEYWORDS: Mining, General packet radio service, Land mines, Algorithm development, Principal component analysis, Detection and tracking algorithms, Ferroelectric LCDs, Distance measurement, Ground penetrating radar, Data mining
We describe several automatic mine detection algorithms in this paper. These methods were tested on real Ground Penetrating Radar (GPR) data and showed dramatic improvement in terms of probability of detection and false alarm rates compared to energy based techniques. The main contributions of this paper are as follows. (1) Only background clutter data, instead of mine data, are needed for the development of the algorithms, which makes collection of data for training and adapting the algorithms to new environment much easier than methods requiring both clutter and mine data for training. (2) The mine detection algorithms are developed in a fairly general form, and thus can be ported to other sensor platforms or future generations of mine detection hardware with little modification. (3) The algorithms require little on-line computation. (4) Adaptation of the algorithms to new environment or mine-fields is done automatically, which reduces human resources and the cost of training.
The U.S. Army has under development a number of systems to detect buried metallic and nonmetallic land mines. Almost all of these systems include a ground penetrating radar (GPR). These systems may be handheld or vehicle mounted and may be designed for close in or for standoff detection. A consensus has not been reached regarding many important system parameters. A discussion of the tradeoffs involving waveform, frequency, bandwidth, downlook angle, scanning methods, polarization, and spatial resolution will be presented. The usefulness of special techniques such as target resonances, synthetic aperture, and multiple polarizations will be discussed. The potential of GPR will be compared with competing sensors. A brief overview of a diverse set of GPR sensors will be presented.
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