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29 April 2008 Exploiting "mineness" for scatterable minefield detection
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In a typical minefield detection problem, the minefield decision is based on the number of detected targets in a given field segment. The detected target locations are obtained by an anomaly detector, such as the RX, using constant target rate (CTR) or constant false alarm rate (CFAR) thresholding. Specific shape and spectral features at the detection locations are used to assign "mineness" or "non-mineness" measures to the detections, which are further used for false alarm mitigation (FM). The remaining detections after FM are used to assign a minefield metric based on a spatial point process (SPP) formulation. This paper investigates how this "mineness" attribute of the detected targets can be exploited to improve the performance of scatterable minefield detection over and above that which is possible by FM. The distribution of the detections in the segment is formulated as a marked point process (MPP), and the minefield decision is based on the log-likelihood ratio test of a binary hypothesis problem. An elegant, linear complexity algorithm is developed to maximize this log-likelihood ratio. An iterative expectation maximization algorithm is used to estimate the unknown probability of the detection of mines. The minefield detection performance, based on SPP with false alarm mitigation and MPP formulation under both CTR and CFAR thresholding methods, is compared using thousands of simulated minefields and background segments.
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Anh Trang, Sanjeev Agarwal, Thomas Broach, and Thomas Smith "Exploiting "mineness" for scatterable minefield detection", Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 695317 (29 April 2008);

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