A particle filter via constrained sampling for nonlinear dynamic systems

Chongyang Hu, Yan Liang, Xiaoxu Wang, Linfeng Xu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This article is concentrated on the particle filtering problem for nonlinear systems with nonlinear equality constraints. Considering the constraint information incorporated into filters can improve the state estimation accuracy, we propose an adaptive constrained particle filter via constrained sampling. First, in order to obtain particles drawn from the constrained important density function, we construct and solve a general optimization function theoretically fusing equality constraints and the importance density function. Furthermore, to reduce the computation time caused by the number of particles, the constrained Kullback-Leiler distance sampling method is given to online adapt the number of particles needed for state estimation. A simulation study in the context of road-confined vehicle tracking demonstrates that the proposed filter outperforms the typical constrained ones for equality constrained dynamic systems.

Original languageEnglish
Pages (from-to)4944-4959
Number of pages16
JournalInternational Journal of Robust and Nonlinear Control
Volume30
Issue number13
DOIs
StatePublished - 10 Sep 2020

Keywords

  • constrained particle filter
  • constrained sampling
  • equality constraints
  • nonlinear dynamic systems
  • state estimation

Fingerprint

Dive into the research topics of 'A particle filter via constrained sampling for nonlinear dynamic systems'. Together they form a unique fingerprint.

Cite this