EM61-3D Discrimination of UXO Using Empirical, Analytic, and Numerical Models

Abstract

The primary goal of this project was to compare three quantitative approaches to modeling EM induction for UXO discrimination: a phenomenological dipole model, a semianalytic theory, and a finite-element numerical method. The secondary goal was to quantify the value of multiple spatial components and time channels. A general, time-dependent triaxial dipole model was developed. The semianalytic theory was not completed; implementation of the numerical method was impractical given allocated resources. The relative merits of additional temporal and spatial information were assessed. Performance was measured by the fraction of false positives for ordnance-like objects at 91% true positives. The best false-positive rate for the full multicomponent, multichannel data was just 5%. However, this excellent performance is largely due to the fact that the ordnance-like objects are larger than the scrap-like objects in this data set. Better generalization may be obtained with discriminants based solely on shape which yielded 32% false positives. This work demonstrated in an internally consistent way the high performance in UXO discrimination that can be achieved with multicomponent, multichannel electromagnetic sensors, as well as the value of relatively simple modeling and discrimination procedures.

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Document Details

Document Type
Technical Report
Publication Date
Dec 24, 2002
Accession Number
ADA480886

Entities

People

  • Alex Becker
  • Pieter Weichman

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Data Sets
  • Detection
  • Detectors
  • Discrimination
  • Eddy Currents
  • Electromagnetic Induction
  • Explosives
  • Finite Element Analysis
  • Magnetic Fields
  • Mean Field Theory
  • Munitions
  • Neural Networks
  • Orientation (Direction)
  • Time Domain
  • Unexploded Ammunition
  • Uxo Detection

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Sensor Fusion and Tracking Systems.
  • Theoretical Analysis.