Random Finite Sets and Sequential Monte Carlo Methods in Multi-Target Tracking

Abstract

Random finite sets provide a rigorous foundation for optimal Bayes multi-target filtering. The major hurdle faced in Bayes multi-target filtering is the inherent computational intractability of the method. Even the Probability Hypothesis Density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multi-target posterior, still involves multiple integrals with no closed forms. In this paper, the authors highlight the relationship between the Radon-Nikodym derivative and set derivative of random finite sets that enable a Sequential Monte Carlo (SMC) implementation of the optimal multi-target filter. In addition, a generalized SMC method to implement the PHD filter also is presented. the SMC PHD filter has an attractive feature -- its computational complexity is independent of the (time-varying) number of targets.

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Document Details

Document Type
Technical Report
Publication Date
Apr 14, 2005
Accession Number
ADA445311

Entities

People

  • Arnaud Doucet
  • Ba-ngu Vo
  • Sumeetpal Singh

Organizations

  • University of Melbourne

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Computational Complexity
  • Filters
  • Filtration
  • Integrals
  • Mathematical Analysis
  • Mathematical Filters
  • Monte Carlo Method
  • Multitarget Tracking
  • Probability
  • Probability Distributions
  • Probability Hypothesis Density Filters
  • Sampling
  • Sequential Monte Carlo Methods
  • Target Tracking

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.