The CPHD Filter with Target Spawning

Daniel S. Bryant, Emmanuel D Delande, Steven Gehly, Jeremie Houssineau, Daniel E Clark, Brandon A. Jones

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
123 Downloads (Pure)


In its classical form, the cardinalized probability hypothesis density (CPHD) filter does not model the appearance of new targets through spawning, yet there are applications for which spawning models more appropriately account for newborn objects when compared to spontaneous birth models. In this paper, we propose a principled derivation of the CPHD filter prediction step including spontaneous birth and spawning. A Gaussian Mixture implementation of the CPHD filter with spawning is then presented, illustrated with three applicable spawning models on a simulated scenario involving two parent targets spawning a total of five objects.

Original languageEnglish
Pages (from-to)1324-1338
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number5
Early online date8 Aug 2016
Publication statusPublished - 1 Mar 2017


  • Bayesian estimation
  • Cardinalized probability hypothesis density (CPHD) filter
  • Multi-object filtering
  • point processes
  • random finite sets
  • target spawning
  • target tracking

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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