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3 May 2016 Explosive hazard detection using synthetic aperture acoustic sensing
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In this paper, we develop an approach to detect explosive hazards designed to attack vehicles from the side of a road, using a side looking synthetic aperture acoustic (SAA) sensor. This is done by first processing the raw data using a back-projection algorithm to form images. Next, an RX prescreener creates a list of possible targets, each with a designated confidence. Initial experiments are performed on libraries of the highest confidence hits for both target and false alarm classes generated by the prescreener. Image chips are extracted using pixel locations derived from the target’s easting and northing. Several feature types are calculated from each image chip, including: histogram of oriented gradients (HOG), and generalized column projection features where the column aggregator takes the form of the minimum, maximum, mean, median, mode, standard deviation, variance, and the one-dimensional fast Fourier transform (FFT). A support vector machine (SVM) classifier is then utilized to evaluate feature type performance during training and testing in order to determine whether the two classes are separable. This will be used to build an online detection system for road-side explosive hazards.
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E. Brewster, J. M. Keller, K. Stone, and M. Popescu "Explosive hazard detection using synthetic aperture acoustic sensing", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231Q (3 May 2016);

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