MEAP: Approximate optimal estimate extraction for the SMC-PHD filter

Tiancheng Li, Juan M. Corchado, Jesus Garcia, Javier Bajo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Multi-estimate extraction (MEE), also referred to as displaying tracks, lies at the core of any multi-target tracking systems, but remains a challenge for the sequential Monte Carlo implementation of the probability hypothesis density (SMC-PHD) filter. In this paper, we recall decision and association techniques to distinguish real measurements of targets from clutter and to associate particles to measurements. The MEE problem is then formulated as a family of parallel single-estimate extraction problems, where the expected a posteriori (EAP) estimator can be employed, namely the multi-EAP (MEAP) estimator. The MEAP estimator is free of iterative clustering computation, computes fast and yields accurate and reliable estimates. Classical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of fast processing speed and best estimation accuracy.

Original languageEnglish
Title of host publicationFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2309-2316
Number of pages8
ISBN (Electronic)9780996452748
StatePublished - 1 Aug 2016
Externally publishedYes
Event19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany
Duration: 5 Jul 20168 Jul 2016

Publication series

NameFUSION 2016 - 19th International Conference on Information Fusion, Proceedings

Conference

Conference19th International Conference on Information Fusion, FUSION 2016
Country/TerritoryGermany
CityHeidelberg
Period5/07/168/07/16

Keywords

  • Multi-target tracking
  • Particle filter
  • PHD filter

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