Accession Number:

ADA433734

Title:

Multi-Sensor Physics-Based Classification of Unexploded Ordnance

Descriptive Note:

Final technical rept. 1 Jul 2002-30 Jun 2004

Corporate Author:

DUKE UNIV DURHAM NC

Personal Author(s):

Report Date:

2005-01-14

Pagination or Media Count:

4.0

Abstract:

The proposed research will concentrate on how the various magnetometer and induction models Duke has developed can be used to support improved UXO discrimination, especially using multisensor data e.g. magnetometer, frequency-domain EMI and time-domain EMI. We will utilize the multiple, offset dipolemoment model in the context of UXO classification, utilizing training data to constrain the inversion. Such constraints will be implemented as priors in a Bayesian setting. The classification algorithms will process the model parameters features determined by the fit to measured data. The features will be processed by several classifiers, including a likelihood ratio, a support-vector machine SVM and a Bayesian relevance-vector machine RVM. The classifiers will be developed assuming knowledge of the target class UXOs only, assuming little or no information concerning the infinite class of false targets. Moreover, the focus will be primarily on small- and medium-sized UXO since the large ordnance will generally be excavated in any case.

Subject Categories:

  • Ammunition and Explosives
  • Electricity and Magnetism

Distribution Statement:

APPROVED FOR PUBLIC RELEASE