Distributed Monte Carlo Information Fusion and Distributed Particle Filtering

Abstract

We present a Monte Carlo solution to the distributed data fusion problem and apply it to distributed particle filtering. The consensus-based fusion algorithm is iterative and it involves the exchange and fusion of empirical posterior densities between neighbouring agents. As the fusion method is Monte Carlo based it is naturally applicable to distributed particlefiltering. Furthermore, the fusion method is applicable to a large class of networks including networks with cycles and dynamic topologies. We demonstrate both distributed fusion and distributed particle filtering by simulating the algorithms on randomly generated graphs

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Document Details

Document Type
Technical Report
Publication Date
Aug 24, 2014
Accession Number
AD1042335

Entities

People

  • Adrian N. Bishop
  • Isaac L. Manuel

Organizations

  • Australian National University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Data Analysis
  • Data Fusion
  • Filters
  • Filtration
  • Information Processing
  • Information Science
  • Kalman Filters
  • Markov Chains
  • Network Topology
  • Probability
  • Sampling
  • Sensor Networks
  • Sequential Monte Carlo Methods
  • Signal Processing
  • Simulations
  • South Africa

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