A Complex Approach to UXO Discrimination: Combining Advanced EMI Forward Models and Statistical Signal Processing

Abstract

The research described in this report was conducted in fulfillment of Project MM-1572, A Complex Approach to UXO Discrimination: Combining Advanced EMI Forward and Statistical Signal Processing, submitted to the Strategic Environmental Research and Development Program (SERDP) in response to the Munitions Management Statement of Need MMSON-07-04, Advanced Technologies for Detection, Discrimination, and Remediation of Munitions and Explosives of Concern (MEC): UXO Technology. The well-known and prohibitive cost of carefully excavating all geophysical anomalies detected at lands contaminated with unexploded ordnance (UXO) is one of the greatest impediments to performing an efficient and thorough cleanup of former battlefields and of Department of Defense (DoD) and Department of Energy (DOE) sites. Innovative discrimination techniques are required that can quickly and reliably distinguish between hazardous UXO and non-hazardous metallic items. The key to success lies in the development of advanced processing techniques that can analyze and process sophisticated magnetic or electromagnetic induction data, with its novel waveforms, ever improving quality, and vector or tensor character, so as to maximize the probability of correct classification and minimize the falsealarm rate.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2012
Accession Number
ADA578937

Entities

People

  • Fridon Shubitidze

Organizations

  • Sky Research

Tags

Communities of Interest

  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Coordinate Systems
  • Data Mining
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Electromagnetic Induction
  • Electromagnetic Induction Sensors
  • Explosives
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Magnetic Fields
  • Munitions
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Uxo Detection

Readers

  • Environmental Engineering.
  • Munitions and Ordnance Engineering
  • Neural Network Machine Learning.