Information Aggregation for IED Identification with GPR, Video, and Electromagnetic Induction: Within Sensor Processing, Multi Sensor Fusion, and Large-Scale Learning

Abstract

The fundamental objectives of this work are to use modern machine learning techniques to (1) develop new algorithms for both prescreening and object discrimination to support the HMDS program; (2) assess the utility of information-theoretic approaches that we have been developing for other sponsors for consideration by the various NVESD programs, particularly the incoming forward looking and handheld programs; (3) develop algorithms for any other sensors of interest to the sponsor and assess their performance. Historically, algorithm development work has included downward and forward looking GPR, IR, hyperspectral, acoustic, seismic, EMI, and video sensing modalities. For HMDS we have been carefully considering robustness issues with respect to target localization for feature extraction, modifying one of our previously developed prescreeners, and investigating convolutional neural networks as a new potentially effective processing algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Jan 24, 2019
Accession Number
AD1071843

Entities

People

  • Leslie M. Collins
  • Peter Torrione

Organizations

  • Duke University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Networks
  • Computer Vision
  • Convolutional Neural Networks
  • Detection
  • Detectors
  • Electromagnetic Induction
  • Electromagnetic Induction Sensors
  • False Alarms
  • Feature Extraction
  • Hidden Markov Models
  • Image Recognition
  • Long-Wavelength Infrared Radiation
  • Machine Learning
  • Neural Networks
  • Supervised Machine Learning
  • Warning Systems

Fields of Study

  • Computer science

Readers

  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks