Approaches to Information Fusion with Spatiotemporal Aspects for Standoff and other Biodefense Information Sources

Abstract

This paper discusses some of the techniques developed at MIT Lincoln Laboratory for information fusion of lidar-based biological standoff sensors, meteorology, point sensors, and potentially other information sources, for biodefense applications. The developed Spatiotemporal Coherence (STC) fusion approach includes phenomenology aspects and approximate uncertainty measures for information corroboration quantification. A supervised machine-learning approach was also developed. Computational experiments involved ground-truth data generated from measurements and by simulation techniques that were developed. The fusion results include performance measures that focus explicitly on the fusion algorithms' effectiveness. Both fusion approaches enable significant false-alarm reduction. Their respective advantages and tradeoffs are examined.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2010
Accession Number
ADA524013

Entities

People

  • Austin Hess
  • Edward C. Wack
  • Jerome J. Braun
  • John Strawbridge
  • Karianne Bergen
  • Robert M. Mays
  • Timothy J. Dasey
  • Yan Glina

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computational Fluid Dynamics
  • Decision Support Systems
  • Detection
  • Detectors
  • False Alarms
  • Information Processing
  • Learning
  • Machine Learning
  • Meteorology
  • Neural Networks
  • Simulations
  • Standoff
  • Supervised Machine Learning
  • Uncertainty
  • United States

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML