The generalized SEA and a statistical signal processing approach applied to UXO discrimination

Shamatava, Irma; Shubitidze, Fridon; Barrowes, Ben; Demidenko, Eugene; Fernández, Juan Pablo; O'Neill, Kevin

doi:doi:10.1117/12.777885

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The generalized SEA and a statistical signal processing approach applied to UXO discrimination

Proc. SPIE 6953, 69531G (2008); http://dx.doi.org/10.1117/12.777885

Monday 17 March 2008

Orlando, FL, USA

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII

Russell S. Harmon, John H. Holloway, Jr., J. Thomas Broach

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Abstract

References (27)

Irma Shamatava and Fridon Shubitidze

Sky Research, Inc. and Dartmouth College

Ben Barrowes

U.S. Army ERDC Cold Regions Research and Engineering Lab.

Eugene Demidenko

Dartmouth College

Juan Pablo Fernández

Sky Research, Inc.

Kevin O'Neill

Sky Research, Inc. and U.S. Army ERDC Cold Regions Research and Engineering Lab.

The prohibitive costs of excavating all geophysical anomalies are well known and are one of the greatest impediments to efficient clean-up of unexploded ordnance (UXO)-contaminated lands at Department of Defense (DoD) and Department of Energy (DOE) sites. Innovative discrimination techniques that can reliably distinguish between hazardous UXO and non-hazardous metallic items are required. The key element to overcoming these difficulties lies in the development of advanced processing techniques that can treat complex data sets to maximize the probability of accurate classification and minimize the false alarm rate. To address these issues, this paper uses a new approach that combines a physically complete EMI forward model called the Generalized Standardized Excitation Approach (GSEA) with a statistical signal processing approach named Mixed Modeling (MM). UXO discrimination requires the inversion of digital geophysical data, which could be divided into two pars: 1) linear - estimating model parameters such as the amplitudes of the responding GSEA sources and 2) non-linear - inverting an object's location and orientation. Usually the data inversion is an ill-posed problem that requires regularization. Determining the regularization parameter is not straightforward, and in many cases depends on personal experience. To overcome this issue, in this paper we employ the statistical approach to estimate regularization parameters from actual data using the un-surprised mixed model approach. In addition, once the non-linear inverse scattering parameters are estimated then for UXO discrimination a covariance matrix and confidence interval are derived. The theoretical basis and practical realization of the combined GSEA-Mixed Model algorithm are demonstrated. Discrimination studies are done for ATC-UXO sets of time-domain EMI data collected at the ERDC UXO test stand site in Vicksburg, Mississippi.

History

Online Apr 14, 2008

Digital Object Identifier

http://dx.doi.org/10.1117/12.777885

Citation

Irma Shamatava, Fridon Shubitidze, Ben Barrowes, Eugene Demidenko, Juan Pablo Fernández and Kevin O'Neill, "The generalized SEA and a statistical signal processing approach applied to UXO discrimination", Proc. SPIE 6953, 69531G (2008); http://dx.doi.org/10.1117/12.777885