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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.
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