Adaptive fuzzy IMM algorithm for uncertain target tracking

Hyun Sik Kim, Joon Goo Park, Dongik Lee

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

In real system application, the interacting multiple model (IMM)-based uncertain target tracking system operates with the following problems: it requires less computing resources as well as a robust performance with respect to the maneuvering such as a sub-model mismatched case, and further, it requires an easy design procedure related to its structures and parameters. To solve these problems, an adaptive fuzzy IMM (AFIMM) algorithm, which is based on well-defined basis sub-models and well-adjusted mode transition probabilities (MTPs), is proposed. The basis sub-models are defined by the detailed analysis in terms of kinematic models as well as the maneuvering property and the MTPs are adjusted by the fuzzy adaptor as well as the fuzzy decision maker. To verify the performance of the proposed algorithm, an airborne target tracking is performed. Simulation results show that the AFIMM effectively solves the problems experienced in the uncertain target tracking system online.

Original languageEnglish
Pages (from-to)1001-1008
Number of pages8
JournalInternational Journal of Control, Automation and Systems
Volume7
Issue number6
DOIs
StatePublished - Dec 2009

Keywords

  • Adaptive fuzzy IMM
  • Basis sub-models
  • Mode transition probabilities
  • Uncertain target

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