Demonstration of Advanced EMI Models for Live-Site UXO Discrimination at Former Camp Butner, North Carolina

Abstract

This demonstration is designed to illustrate the discrimination performance at a challenging livesite of a suite of advanced electromagnetic induction (EMI) modeling approaches developed to go beyond the simple dipole model in accuracy and predictive ability. The core of the suite consists of the orthonormalized volume magnetic source (ONVMS) model for target characterization, a target-counting pre-processing procedure based on joint diagonalization (JD), and an implementation of the differential evolution (DE) algorithm for nonlinear optimization used to locate targets. The study used cued data sets collected at Camp Butner in North Carolina using two next-generation EMI sensors, the Geometrics MetalMapper (MM) and the Timedomain Electro-Magnetic Towed Array Detection System (TEMTADS) developed by the NRL and G&G Sciences. The site was contaminated with fuzes and a mix of 37-mm and 105-mm munitions. Each data set was inverted with the purpose of estimating the number of targets producing each anomaly and the parameters associated with each target, both extrinsic its orientation, location and depth and intrinsic its total volume magnetic source amplitude (ONVMS), which depends on its size, shape and material properties. The inverted intrinsic parameters were then used to classify the targets, and in the end we generated sensor-specific dig-lists for each EMI instrument and submitted them to the Institute of Defense Analyses (IDA) for independent scoring.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2012
Accession Number
ADA575235

Entities

People

  • Fridon Shubitidze

Organizations

  • Sky Research

Tags

Communities of Interest

  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Data Analysis
  • Data Mining
  • Data Sets
  • Department Of Defense
  • Detection
  • Detectors
  • Electromagnetic Induction
  • Electromagnetic Induction Sensors
  • Information Science
  • Magnetic Fields
  • Munitions
  • North Carolina
  • Remote Sensing
  • Signal Processing
  • Supervised Machine Learning
  • Unexploded Ammunition
  • Uxo Detection

Readers

  • Computational Modeling and Simulation
  • Military/Explosive Ordnance Disposal (EOD) Technology