Statistical Classification of Buried Unexploded Ordnance Using Nonparametric Prior Models
نویسندگان
چکیده
منابع مشابه
Statistical Methods for Unexploded Ordnance Discrimination
We propose statistical processing methods and performance analysis techniques for discrimination and localization of Unexploded Ordnance (UXOs) using EMI sensors based on nonparametrically defined prior probability density functions for target-relevant features. In the first part of this thesis, new sets of UXO discrimination methods using these nonparametric prior models are introduced where w...
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A procedure is described for computing range and orientation invariant spectral signatures of buried unexploded ordnance (UXO) from electromagnetic induction (EMI) data. The normalized eigenvalues of the magnetic polarizability tensor that characterizes the target response are used as the orientation-invariant spectral signatures. It is shown that the eigenvalues can be normalized with respect ...
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According to the Department of Defense, over 10 million acres of land in the US need to be cleared of buried unexploded ordnance (UXO). Worldwide, UXO injures thousands each year. Cleanup costs are prohibitively expensive due to the difficulties in discriminating buried UXO from other inert non-UXO objects. Government agencies are actively searching for improved sensor methodologies to detect a...
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An ultra-wideband (UWB) synthetic aperture radar (SAR) system is investigated for the detection of former bombing ranges, littered by unexploded ordnance (UXO). The objective is detection of a high enough percentage of surface and shallow-buried UXO, with a low enough false-alarm rate, such that a former range can be detected. The physics of UWB SAR scattering is exploited in the context of a h...
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ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2007
ISSN: 0196-2892
DOI: 10.1109/tgrs.2007.900681