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Proceedings Paper

Combining dipole and mixed model approaches for UXO discrimination
Author(s): Fridon Shubitidze; Eugene Demidenko; Benjamin E. Barrowes; Irma Shamatava; Juan P. Fernández; Kevin O'Neill
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Paper Abstract

A multi dipole (MD) model is combined with a statistical algorithm called the mixed model to discriminate between objects of interest, such as unexploded ordnance (UXO), and innocuous items. In the multi dipole model (an extended version of the single dipole model), electromagnetic induction (EMI) responses for bodies of revolution (BOR) are approximated with a set of dipoles placed along the axis of symmetry of the objects. The model accurately takes into account the scatterer's heterogeneity along its axis of symmetry and is fast enough to invert digital geophysical data for discrimination purposes in real/near real time. Determining the amplitudes of the multi dipoles is an ill-posed problem that requires regularization. Obtaining the regularization parameters is not straightforward and in many cases is done via impractical supervised approaches. To overcome this problem, in this paper we combine a new statistical approach called the mixed model with the multi dipole model. Mixed modeling (MM) can be viewed as a generalization of the empirical Bayesian approach. It assumes that the forward model is not perfect: i.e., the model parameters (the amplitudes of the responding multi magnetic dipoles) contain random noise with zero mean and constant variance. Based on these assumptions, the method derives the regularization parameter from the variance of the least square error between the model and actual data using standard linear regression. Numerical results are presented to illustrate the theoretical basis and practical realization of the combined MD-mixed model (MD-MM) algorithm for UXO discrimination under real field conditions. In addition, a new condensed algorithm for determining the location and orientation of buried objects is introduced and tested against the ESTCP pilot discrimination study dynamic data set.

Paper Details

Date Published: 29 April 2008
PDF: 12 pages
Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 695305 (29 April 2008); doi: 10.1117/12.777868
Show Author Affiliations
Fridon Shubitidze, Dartmouth College (United States)
Sky Research, Inc. (United States)
Eugene Demidenko, Dartmouth College (United States)
Benjamin E. Barrowes, U.S. Army ERDC Cold Regions Research and Engineering Lab. (United States)
Irma Shamatava, Sky Research, Inc. (United States)
Dartmouth College (United States)
Juan P. Fernández, Dartmouth College (United States)
Kevin O'Neill, Dartmouth College (United States)
U.S. Army ERDC Cold Regions Research and Engineering Lab. (United States)


Published in SPIE Proceedings Vol. 6953:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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