Multiple target tracking with constrained motion using particle filtering methods

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we propose the constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information into a particle filter to track multiple targets. We represent deterministic or stochastic constraints on target motion as a likelihood function that is incorporated into the particle filter proposal density. Using Monte Carlo simulations, we demonstrate that this approach improves tracking performance while reducing computational cost relative to the independent partition particle filter with and without a constraint likelihood function.

Original languageEnglish (US)
Title of host publicationIEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Pages85-88
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventIEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Puerto Vallarta, Mexico
Duration: Dec 13 2005Dec 15 2005

Publication series

NameIEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Volume2005

Other

OtherIEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
CountryMexico
CityPuerto Vallarta
Period12/13/0512/15/05

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Kyriakides, I., Morrell, D., & Papandreou-Suppappola, A. (2005). Multiple target tracking with constrained motion using particle filtering methods. In IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (pp. 85-88). [1574190] (IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing; Vol. 2005). https://doi.org/10.1109/CAMAP.2005.1574190