A Feasibility Study on Using Physics-Based Modeler Outputs to Train Probabilistic Neural Networks for UXO Classification

Abstract

A probabilistic neural network (PNN) has been applied to the detection and classification of unexploded ordnance (UXO) measured using magnetometry data collected using the Multi-sensor Towed Array Detection System (MTADS). Physical parameters obtained from a physics based modeler were used to describe the UXO and scrap targets found at three sites: Badlands Bombing Range (BBR) Target 1 and 2 and the Former Buckley Field. The PNN was trained and tested using cross validation (CV) software developed at NRL. The PNN was able to correctly identify between 84% to 94% of the targets. By adjusting the probability threshold, further improvements in the discrimination of UXO were possible: 96% of the UXO were correctly identified for BBR Target 1, 100% for BBR Target 2, and 94% for the former Buckley Field. The ability to train using one site (BBR target 2) and predict another (BBR Target 1) was successful with 95% of the UXO correctly identified and a false alarm rate of 35%.

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Document Details

Document Type
Technical Report
Publication Date
Apr 29, 1999
Accession Number
ADA364744

Entities

People

  • Jim R. McDonald
  • R. E. Shaffer
  • S. J. Hart
  • S. L. Rose-pehrsson

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Data Science
  • Data Sets
  • Detection
  • Detectors
  • False Alarms
  • Feasibility Studies
  • Information Science
  • Munitions
  • Neural Networks
  • Pattern Recognition
  • Probability
  • Two Dimensional
  • Unexploded Ammunition
  • Uxo Detection
  • Warning Systems

Readers

  • Computational Modeling and Simulation
  • Military/Explosive Ordnance Disposal (EOD) Technology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks