The NVESD Mine Lane Facility has recently undergone an extensive renovation. It now consists of an indoor, dry lane portion, a greenhouse portion with moisture-controlled lanes, a control room, and two outdoor lanes. The indoor structure contains six mine lanes, each approximately 2.5m (width) × 1.2m (depth) × 33m(length). These lanes contain six different soil types: magnetite/sand, silt, crusher run gravel (bluestone gravel), bank run gravel (tan gravel), red clay, and white sand. An automated trolley system is used for mounting the various mine detection systems and sensors under test. Data acquisition and data logging is fully automated. The greenhouse structure was added to provide moisture controlled lanes for measuring the effect of moisture on sensor effectiveness. A gantry type crane was installed to permit remotely controlled positioning of a sensor package over any portion of the greenhouse lanes at elevations from ground level up to 5m without shadowing the target area. The roof of the greenhouse is motorized, and can be rolled back to allow full solar loading. A control room overlooking the lanes is complete with recording and monitoring devices and contains controls to operate the trolleys. A facility overview is presented and typical results from recent data collection exercises are presented.
KEYWORDS: Land mines, General packet radio service, Detection and tracking algorithms, Synthetic aperture radar, Reflection, Data processing, Sensors, Mining, Radar, Signal processing
For downward looking GPR landmine detection systems, the return from the ground surface, i.e., the ground bounce, often surpasses the actual mine return and makes it almost impossible to detect the landmines, especially the buried plastic landmines. The ground bounce is difficult to remove due to the roughness of the ground surface and/or the changing soil conditions. In this paper, a robust and efficient ground bounce removal algorithm, referred to as ASaS (Adaptive Shift and Scale), is presented. ASaS takes into account the variations of the ground bounce by adaptively selecting a reference ground bounce. The shifted and scaled version of the reference ground bounce is used as the estimate of the ground bounce in the current scan. Two adaptive reference selection schemes for ASaS are given and compared with each other. Experimental results based on the data collected by the PSI GPSAR system are used to demonstrate the effectiveness of the adaptive schemes.
KEYWORDS: Sensors, General packet radio service, Image fusion, Laser Doppler velocimetry, Mining, Detection and tracking algorithms, Algorithm development, Land mines, Data modeling, Data fusion
Current research in minefield detection 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. Consequently, the NVESD sponsored Signal Processing and Algorithm Development for Robust Mine Detection (SPAD) Program is currently focusing on the development of computationally efficient and robust detection algorithms applicable to a variety of sensors and on the development of a robust decision level fusion algorithm that exploits these detectors. One SPAD detection technique, called the Ellipse Detector, has been previously reported in the open literature. We briefly report on the continued robust performance of this detector on some new sensor output. We also report on another robust detector developed for sensors that produce output not suitable for the Ellipse Detector. However, the focus of this paper is on the SPAD decision level fusion algorithm, called the Piecewise Level Fusion Algorithm (PLFA). We emphasize the robustness and flexibility of the PLFA architecture by describing its performance and results for both multisensor and multilook fusion.
Planning Systems Incorporated (PSI) has developed a promising Ground Penetrating Synthetic Aperture Radar (GPSAR) system to detect buried landmines. GPSAR can be used to generate three-dimensional (3-D) mine images. It has been shown that the SAR processing in the PSI GPSAR system can greatly improve the image quality and hence the mine (especially plastic mine) detection performance. In this paper, two special issues on SAR processing for the PSI system are addressed. One issue is the analysis of the effect of the underground electromagnetic (EM) wave propagation velocity uncertainty on SAR processing and the other is channel mismatch on SAR processing. Since the EM wave propagation velocity in the soil depends on many factors and changes from one location to another, velocity uncertainty is inevitable. However, we have found that the PSI GPSAR system is very robust against the velocity uncertainty. More specifically, velocity uncertainty does not defocus the image but only scales the image along the depth dimension, and hence will not affect the mine detection performance. Another issue is how to select a good SAR processing scheme for the PSI system. Because the radar footprint is 2-D (along-track and cross-track dimensions), 2-D SAR processing may be used. However, the effectiveness of the 2-D SAR processing depends on the coherence of the radar antenna system. Moreover, the computational expense of the 2-D SAR processing is much higher than that of the 1-D SAR processing (along-track dimension only). We have found that due to the channel mismatch of the PSI system, the 2-D SAR processing does not greatly improve the quality of the SAR images when compared with 1-D SAR processing. Hence, without proper antenna calibration, the computationally more efficient 1-D SAR processing may be preferred for the PSI system.
KEYWORDS: Radar, Data modeling, General packet radio service, Antennas, Ground penetrating radar, Target detection, Thulium, Land mines, Mining, Detection and tracking algorithms
Techniques using ground-penetrating radar (GPR) for the detection of targets such as abandoned landmines or unexploded ordnance (UXO) buried under the ground surface continue to receive considerable attention especially in the area of signal processing. In this paper we consider the problem of eliminating the so-called ground-bounce effect, which is due to the specular ground surface reflections of the radar signal. The ground-bounce returns are often significantly stronger than the reflection from a target and pose a challenging problem. Existing techniques commonly assume that the ground response is constant as the radar equipment moves along a track. By using measured data, we show that this is, for several reasons, an unrealistic assumption. Instead, we consider a semi-parametric model for the ground-bounce that is in better agreement with observed data. Furthermore, we show how this model can be used to derive an accurate and robust but yet conceptually simple algorithm for the removal of the ground return. We demonstrate our technique using data recorded by an ultra-wideband GPR on a U.S. Army test range.
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.
The US Army has a major interest in the development of an airborne system capable of detecting surface and buried anti-tank mines under all-weather, day/night conditions. While the hardware components are essentially an airborne reconnaissance system generating high resolution image data, it is the processing algorithm which provides the mine/minefield detection capability. During the spring and summer of 1997, flight test of several minefield detection systems were concluded at Ft. Huachuca, AZ. The systems tested represented three major technology approaches, namely passive thermal IR, active near IR laser imager for polarization based discrimination and hyperspectral. Image data generated by these sensors were recorded to tape or other media and subsequently processed using various minefield detection algorithm approaches.
The airborne standoff minefield detection systems (ASTAMIDS) is an airborne imaging system designed for deployment as a modular mission payload on an unmanned aerial vehicle and capable of detecting surface and buried anti-tank mines under all-weather, day/night conditions. Its primary mission is to support a forward maneuver unit with real time intelligence regarding the existence and extent of minefields in their operational area. The ASTAMIDS development effort currently consists of two parallel technical approaches, passive thermal IR sensor technology in one case and an active multi-channel sensor utilizing passive thermal IR coregistered with a near IR laser polarization data for the other case. The minefield detection capability of this system is a result of signal processing of image data. Due to the large quantities of data generated by an imaging sensor even at modest speeds of an unmanned aerial vehicle, manual exploitation of this data is not feasible in a real time tactical environment and therefore computer aided target feature extraction is a necessity to provide detection cues. Our development efforts over the past several years have concentrated on mine and minefield detection algorithms, the hardware necessary to execute these algorithms in real time, and the tools with which to measure detection performance.
This report presents findings based on an analysis of the thermal characteristics of live US Army anti-tank mines and concrete slurry-filled M75 surrogates. The US Army's Airborne Standoff Minefield Detection System program relies on the use of surrogate mines to provide their prime contractors with targets to test and develop their systems. Analysis of 8-12 mm sensor image data collected over a period of days at Ft. A.P. Hill, Virginia indicates that the concrete slurry-filled M75 surrogates have diurnal thermal infrared signatures that are very similar to those of live M75 mines, and are therefore good mine surrogates.
The US Army Night Vision and Electronic Sensors Directorate's Countermine Division has received and evaluated automatic target recognition (ATR) algorithms for a number of years. As part of the Army's Advanced Standoff Mine Detection System (ASTAMIDS) program, Camber Corporation has developed an algorithm simulation and evaluation testbed (ASET) that is capable of testing various ATR algorithms on a variety of images. The ASET tool has been designed to support any image type and contains an extensive database of images from ASTAMIDS and previous mine detection programs including REMIDS and AMIDARS, as well as data from SAR and other sources that may be queried and used to exercise a given algorithm. Algorithms can be easily converted into the ASET environment and executed on selected imagery. ASET accurately simulates the execution of algorithms, tests and grades their performance, and allows manipulation and enhancements of the algorithms during execution. This software runs on a variety of computer platforms and allows ATR algorithms to be evaluated under a variety of circumstances.
This paper describes the work completed by Martin Marietta in support of the U.S. Army's standoff minefield detection system, advanced technology transition demonstration. This paper discusses the high priority and urgent need for the standoff mine detection system within the Army Combat Engineers, it presents the results of the successful application of non developmental technology/hardware in an airborne mine/minefield detection system, and it discusses the significant payoff of applying advanced ATR and high speed parallel processing. The technologies discussed include the IR imager as the source of mine imagery, advanced image processing algorithms including neural nets, and a high speed parallel processor unique to Martin Marietta called GAPP (geometric arithmetic parallel processor).
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