Improved SMC implementation of the PHD filter

Branko Ristic, Daniel Clark, Ba N. Vo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

158 Citations (Scopus)


The paper makes two contributions. First, a new formulation of the PHD filter which distinguishes between persistent and newborn objects is presented. This formulation results in an efficient sequential Monte Carlo (SMC) implementation of the PHD filter, where the placement of newborn object particles is determined by the measurements. The second contribution is a novel method for the state and error estimation from an SMC implementation of the PHD filter. Instead of clustering the particles in an ad-hoc manner after the update step (which is the current approach), we perform state estimation and, if required, particle clustering, within the update step in an exact and principled manner. Numerical simulations indicate a significant improvement in the estimation accuracy of the proposed SMC-PHD filter. © Commonwealth of Australia.

Original languageEnglish
Title of host publication13th Conference on Information Fusion, Fusion 2010
Publication statusPublished - 2010
Event13th Conference on Information Fusion - Edinburgh, United Kingdom
Duration: 26 Jul 201029 Jul 2010


Conference13th Conference on Information Fusion
Abbreviated titleFusion 2010
Country/TerritoryUnited Kingdom


  • Multi-object estimation
  • Particle filter
  • PHD filter
  • Sequential Monte Carlo
  • Tracking


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