Bayesian Multiple Target Filtering Using Random Finite Sets

Ba Ngu Vo*, Ba Tuong Vo, Daniel E Clark

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)


The random finite set (RFS) approach, introduced by Mahler as finite set statistics (FISST), is an elegant Bayesian formulation of multitarget filtering based on RFS theory. This chapter describes the RFS approach to multitarget tracking. It focuses on RFS-based algorithms such as the probability hypothesis density (PHD), and cardinalized PHD (CPHD) filters. The chapter also focuses on the recent developments such as the multitarget multi-Bernoulli (MeMBer) filters. An overview of the developments in the RFS approach and the PHD/CPHD filters is also given. The chapter examines the fundamental notion of miss distance or estimation error for multiple targets. It evaluates the PHD/CPHD filters and their Gaussian mixture implementations. Finally, the chapter outlines the MeMBer filter as another approximation approach to the multitarget filtering problem.

Original languageEnglish
Title of host publicationIntegrated Tracking, Classification, and Sensor Management
Subtitle of host publicationTheory and Applications
Number of pages52
ISBN (Electronic)9781118450550
ISBN (Print)9780470639054
Publication statusPublished - 2013


  • Bayesian multitarget filtering
  • Finite set statistics (FISST)
  • Multitarget miss distances
  • Multitarget multi-Bernoulli (MeMBer) filters
  • Multitarget tracking
  • Probability hypothesis density (PHD) filter
  • Random finite set (RFS) approach

ASJC Scopus subject areas

  • Engineering(all)


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