Bayesian Multiple Target Filtering Using Random Finite Sets

Ba Ngu Vo, Ba Tuong Vo, Daniel E Clark

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

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
PublisherWiley
Pages75-126
Number of pages52
ISBN (Electronic)9781118450550
ISBN (Print)9780470639054
DOIs
Publication statusPublished - 2013

Keywords

  • 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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  • Cite this

    Vo, B. N., Vo, B. T., & Clark, D. E. (2013). Bayesian Multiple Target Filtering Using Random Finite Sets. In Integrated Tracking, Classification, and Sensor Management: Theory and Applications (pp. 75-126). Wiley. https://doi.org/10.1002/9781118450550.ch03