Optimizing Uncertainty in Dempster-Shafer Detectors Fusing Multi-Sensor Data

Abstract

Fusion algorithms based on the Dempster-Shafer (DS) Theory of Evidence lack a universally standard method for automatically assigning probability mass to the "don't know" hypothesis for a particular input. For example, when fusing automatic target detection (ATD) algorithm outputs from multiple sensors, one must associate a measure of uncertainty with the output from the ATD algorithm of each sensor. We describe such a fusion algorithm, developed using the DS formalism, and present a method for automatically determining the required assignment of uncertainty. We also evaluate the entire procedure using simulated data and receiver operating characteristic curves.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2009
Accession Number
ADA494600

Entities

People

  • Kenneth I. Ranney
  • Nasser M. Nasrabadi

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Automatic
  • Data Sets
  • Detection
  • Detectors
  • False Alarms
  • Military Research
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Standards
  • Target Detection
  • Target Recognition
  • Uncertainty
  • Warning Systems

Readers

  • Computational Modeling and Simulation
  • Sensor Fusion and Tracking Systems.