Kinematic and Attribute Fusion Using a Bayesian Belief Network Framework

Abstract

The focus of tracking applications has traditionally centred on kinematic state estimation. However, attribute information has the potential to not only provide identity and class information, but it may also improve data association and kinematic tracking performance, Bayesian Belief Networks provide a framework for specifying the dependencies between kinematic and attribute states. Algorithms based on this framework are developed for joint kinematic and attribute data association, kinematic tracking, attribute state estimation, and joint kinematic and attribute tracking. The algorithms are demonstrated using simulated tracking scenarios.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2006
Accession Number
ADA458785

Entities

People

  • Mark L. Krieg

Organizations

  • Defence Science and Technology Group

Tags

Communities of Interest

  • C4I
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Data Processing
  • Detection
  • Detectors
  • Kalman Filters
  • Mechanical Engineering
  • Models
  • Probabilistic Models
  • Probability
  • Radar
  • Simulations
  • Surveillance Radar
  • Target Classification
  • Target Recognition
  • Target Tracking
  • Warfare

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks