Convergence results for the particle PHD filter

Daniel Edward Clark, Judith Bell

Research output: Contribution to journalArticlepeer-review

118 Citations (Scopus)


Bayesian single-target tracking techniques can be extended to a multiple-target environment by viewing the multiple-target state as a random finite set, but evaluating the multiple-target posterior distribution is currently computationally intractable for real-time applications. A practical alternative to the optimal Bayes multitarget filter is the probability hypothesis density (PHD) filter, which propagates the first-order moment of the multitarget posterior instead of the posterior distribution itself. It has been shown that the PHD is the best-fit approximation of the multitarget posterior in an information-theoretic sense. The method avoids the need for explicit data association, as the target states are viewed as a single global target state, and the identities of the targets are not part of the tracking framework. Sequential Monte Carlo approximations of the PHD using particle filter techniques have been implemented, showing the potential of this technique for real-time tracking applications. This paper presents mathematical proofs of convergence for the particle filtering algorithm and gives bounds for the mean-square error. © 2006 IEEE.

Original languageEnglish
Pages (from-to)2652-2661
Number of pages10
JournalIEEE Transactions on Signal Processing
Issue number7
Publication statusPublished - Jul 2006


  • Multitarget tracking
  • Optimal filtering
  • Particle filter
  • Point processes
  • Random sets
  • Sequential Monte Carlo


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