Newer detectors are growing in capability to discriminate those signals measured over mines from those signals that can be causally related to local variations in the soil. Monitoring and measuring the key properties governing these local variations are being looked at increasingly as a means to predict performance measures for given detectors, as well as to counter the occurrence of such signals in an effort to minimize false alarms. Currently, an ongoing government research project working to develop enhancements to the Handheld Standoff Mine Detection System (HSTAMIDS) technology resulted in a series of data collections acquired in four different types of soil environments: 1) temperate/loamy, 2) temperate/grassy/gravel, 3) arid/gravel/sand, and 4) tropical/laterite. At each of these locations, data was collected using the HSTAMIDS technology to provide a range of environmental conditions against which the performance of this handheld detector could be assessed. This project is obtaining similar electrical and magnetic measurements in these areas to use these measurements to monitor any changes in detection performance that might be introduced due to local soil variations, as well as to provide a preliminary estimate of the robustness of future HSTAMIDS detection enhancements across a variety of environments.
The assumption is that removal of elements of clutter from the frequency stepped ground penetration radar (GPR) signal data will improve the performance of any detection algorithms. Clutter comes in the form of internal system interference, cross-coupling signals between antennas, and soil artifacts (soil layers, rocks, non-homogeneous material, grass, etc.). The assumption is that the frequency stepped radar has a number of steps that cover a fixed bandwidth, and that the radar is phase coherent from step to step and over time. Processing consists of transforming the signal data into the spatial-frequency dimension and applying a set of filters, and then transforming into the range (bandwidth compression) dimension. The developed filters remove spectral components that are associated with signal returns from clutter elements. Examples using data from the US Army AN/PSS-14 mine detection system operating over inert mines are presented.
A government-funded effort was initiated to further develop algorithms based on the technology used in the U.S. Army’s latest handheld standoff mine detection system (HSTAMIDS). To this end, a complete multisensor (EM/GPR) baseline signature data set was acquired in the spring of 2003 over targets of interest for landmine detection. These were provided at a government-run test site in the eastern U.S. where hundreds of buried inert mines and discrete clutter objects are available for such signature measurements. Bringing the HSTAMIDS detector technology to this site, in conjunction with a tethered data acquisition hardware and platforms, resulted in a complete baseline multisensor signature data collection. Due to the multisensor nature of the HSTAMIDS technology, the properties of this data collection include total and real-time collocation of electromagnetic and radar sensors. Processed examples of signatures of objects of interest from this baseline signature data set are presented here, along with a summary of the use to which this data set has been put so far. The means for future requests for access to the baseline data set by individual researchers for further algorithm work are also detailed.
EMI sensors are used extensively to detect landmines, and operate by detecting the metal that is present in mines. However, mines vary in their construction form metal-cased varieties with a large mass of metal to plastic-cased varieties with minute amounts of metal. Unfortunately, there is often a significant amount of metallic clutter present in the environment. Consequently, EMI sensors that utilize traditional detection algorithms based solely on metal content suffer form large false alarm rates. We have at least partially mitigated this false alarm problem for high- metal content mines by developing statistical algorithms that exploit phenomenological models of the underlying physics. The Joint UXO Coordination Office (JUXOCU) is sponsoring a series of experiments designed to establish a performance baseline for a variety of sensors. The experiments to dat have focused on detection and discrimination of low-metallic content mines. This baseline will be used to measure the potential improvements in performance offered by advanced signal processing algorithms This paper describes the result of several experiments performed in conjunction with the JUXOCU effort. In our preliminary work, statistical algorithms have been applied specifically to the problem of detection of low-metal mines, and dramatic performance improvements have been observed with respect to the baseline performance. However, these algorithms improvements have been observed with respect to the baseline performance. However, these algorithms were statistical in nature, did not incorporate phenomenological models, and exploited spatial information. The tradeoffs among these various factors are explored in this paper, along with the performance of alternative statistical approaches. In addition, approaches to classification of the mine-type are discussed and the performance of such classifiers is presented.
Conference Committee Involvement (1)
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII
10 April 2017 | Anaheim, California, United States