An Empirical Study into the Use of Chernoff Information for Robust, Distributed Fusion of Gaussian Mixture Models

Abstract

This paper considers the problem of developing algorithms for the distributed fusion of Gaussian Mixture Models through the use of Chernoff information. We derive a first order approximation and show that, in a distributed tracking problem in which sensor nodes are equipped with only range-only or bearing-only sensors, it yields consistent estimates.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2006
Accession Number
ADA498945

Entities

People

  • Simon J. Julier

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Data Fusion
  • Detectors
  • Distribution Functions
  • Errors
  • Filters
  • Kalman Filters
  • Multiple Hypothesis Tracking
  • Network Topology
  • Probability
  • Probability Distributions
  • Random Variables
  • Sensor Fusion
  • Target Tracking
  • Topology

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Sensor Fusion and Tracking Systems.