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Performance and complexity analysis of adaptive particle filtering for tracking applications

  • Stony Brook University

Research output: Contribution to journalConference articlepeer-review

29 Scopus citations

Abstract

This paper provides a performance and complexity analysis of particle filtering as applied to real-time object tracking. The number of particles and the sampling rate strongly influence the performance of particle filters, but more importantly they affect very much their complexity. In the paper, we also propose a particle filter that changes the number of used particles during filtering, where the number of particles is employed for making decisions about performing resampling. The performance of the proposed particle filters is demonstrated on the bearings-only tracking problem.

Original languageEnglish
Pages (from-to)853-857
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume1
StatePublished - 2002
EventThe Thirty-Sixth Asilomar Conference on Signals Systems and Computers - Pacific Groove, CA, United States
Duration: Nov 3 2002Nov 6 2002

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