Model-set adaptation using a fuzzy Kalman filter

Zhen Ding, Henry Leung, Chun Chung Chan, Zhiwen Zhu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


In this paper, a fuzzy Kalman filter (KF) is proposed to combat the model-set adaptation problem of multiple model estimation. The fuzzy KF is found to be able to more exactly extract dynamic information of target maneuvers. It uses a set of fuzzy rules to adaptively control the process noise covariance of the KF and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then incorporated into an interacting multiple model (IMM) algorithm, hence, a fuzzy IMM (FIMM) algorithm is obtained. The performance of the FIMM algorithm is compared with that of an adaptive IMM (AIMM) algorithm using real radar data. Simulation result shows that the FIMM algorithm greatly outperforms the AIMM algorithm in terms of both the root mean square prediction error and the number of track loss.
Original languageEnglish
Pages (from-to)799-812
Number of pages14
JournalMathematical and Computer Modelling
Issue number7-8
Publication statusPublished - 16 Oct 2001


  • Adaptive IMM algorithm
  • Fuzzy Kalman filter
  • IMM algorithm
  • Model-set adaptation
  • Target tracking

ASJC Scopus subject areas

  • Information Systems and Management
  • Control and Systems Engineering
  • Applied Mathematics
  • Computational Mathematics
  • Modelling and Simulation


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