An awareness of activities in operational environments is key to the U.S. Army’s strategy and many sensors and spectral regimes are employed to this end. Advances in wide-spectrum acoustic sensors and compact high performance computational hardware have created opportunities for enhancing awareness. The Engineer Research and Development Center (ERDC) is researching infrasound sensing as a means of persistent, remote monitoring to provide battlefield awareness. Machine learning techniques are used to identify unique signatures in the battlespace’s infrasonic environment. Given the limited number of labeled data sets, unsupervised Gaussian Mixture Modeling (GMM) is applied to identify these signatures utilizing the Short-term Fourier transform (STFT) and resulting Power Spectrum Density (PSD). This study describes the process of sorting collected infrasound data into categories based on PSDs for application to GMM algorithms that identify a characteristic class labeling. Labels in relatively short time frames are then associated with features seen throughout a 24 hour cycle to produce synthetic samples. Several Support Vector Machines are trained and used to separate in-class verses outlier features in time segments of new data. Outlier counts exceeding a threshold, typically 50%, label new data segments as novel and subject to further processing. Finally, efforts are d escribed f or directional focusing the array using multiple elements/sensors to localize signatures or to emphasize the signatures from different directions. GPU accelerations will be applied wherever possible to improve local bandwidth and throughput.
This paper describes the relative polarization and reflectance characterization of background and selected target items to demonstrate the differences material type and source wavelength have on these measurements. The advanced reflectance and polarization instrument (ARPI) was modified to allow three lasers with different wavelengths to be used. This allowed for similar spot size, location, and angles to be used to collect the measurements. ARPI was used to collect polarized and cross-polarized returns from the polarized laser source at an incident angle of 0, 5, 10, and 20 degrees. These measurements were used to calculate the relative percent polarization and percent reflectance.
Analysis of the measured relative polarization and reflectance consists of single wavelength and multiwavelength comparisons with man-made and background items. A direct comparison is made between natural and man-made materials and different wavelengths of light. This careful comparison of differences between wavelengths will demonstrate which of the wavelengths produces the best and most consistent separation between background and manmade items. Our preliminary analysis shows that most man-made items give different polarization and reflectance returns than background items. Also, the analysis shows nominal variability between the three different wavelengths for background items and man-made items.
This paper analyzes the UXO classification capabilities of the GEM-3 using data collected for the Advanced UXO Detection/Discrimination Technology Demonstration at the U.S. Army Jefferson Proving Ground (JPG), Madison, Indiana. The approach taken in the US Army Engineer Research and Development Center (ERDC) analysis of the performance of the GEM-3 at JPG was to extract data points collected near each of the actual target locations and compare them to the calibration data acquired with known targets at the beginning of the demonstration. This was done to determine how well the data collected near each actual target matched the calibration signatures for the same ordnance type and the extent to which the data could be differentiated from other ordnance types and non-ordnance clutter. Classification of the targets was performed using a simple template-matching algorithm. This procedure resulted in an exact classification match for nearly half of the targets for which calibration data were available and a match to a similarly sized target for more than two-thirds of the medium and large targets. The sensor coverage of the test areas and the effect of test parameters such as ordnance size and depth on classification performance were also examined. New data were acquired with the GEM-3 to investigate the statistical variability of the instrument.
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