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Proceedings Paper

Man-portable vector EMI instrument data characterization using the NSMS method
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Paper Abstract

The Man Portable Vector (MPV) instrument is a time-domain handheld electromagnetic induction (EMI) instrument with five vector receivers and subcentimeter positioning accuracy. For cued interrogations, the MPV is designed to discriminate unexploded ordnance (UXO) from non-UXO using models ranging from the simple dipole model to physically complete models such as the Normalized Surface Magnetic Source (NSMS) method. The MPV acquires both EMI data and position at a 10Hz sampling rate resulting in 150 data points per second at each of a user selectable number time channels (typically 30-90) starting at 100 microseconds. Several factors might limit the usefulness of this data under real world conditions including an excess of usable data, noise in the position data, and insufficient coverage of anomalies. In this paper, we investigate the impact these factors have on the accuracy of discrimination results based on both static and dynamic MPV data. We investigate the effect of using only a subset of the data along with averaging techniques to reduce the amount of MPV data from a single anomaly. In addition, we inject various levels of noise into the position of the MPV in order to gauge the robustness of the discrimination results. Data is also selectively considered based on number of receivers and vector component(s). Results suggest that remarkably few data points are required for accurate discrimination results and that the vector receivers and low hardware noise of the MPV lead to robust results even with sparse data or noisy positional data.

Paper Details

Date Published: 4 May 2009
PDF: 13 pages
Proc. SPIE 7303, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV, 73030V (4 May 2009); doi: 10.1117/12.818804
Show Author Affiliations
Benjamin E. Barrowes, U.S. Army Engineer Research and Development Ctr. (United States)
Dartmouth College (United States)
Fridon Shubitidze, Dartmouth College (United States)
Juan P. Fernández, Dartmouth College (United States)
Irma Shamatava, Dartmouth College (United States)
Kevin A. ONeill, U.S. Army Engineer Research and Development Ctr. (United States)
Dartmouth College (United States)


Published in SPIE Proceedings Vol. 7303:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)

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