Model-set adaptation using a fuzzy Kalman filter

Zhen Ding, Henry Leung, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

10 Citations (Scopus)


In this paper, a fuzzy Kalman filter is proposed to combat the model-set adaptation problem since it is found to be able to extract more exactly dynamic information. The fuzzy Kalman filter uses a set of fuzzy rules to adaptively control the noise covariance and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then combined with an 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 target tracking data. Simulation result shows that the FIMM algorithm outperforms the AIMM algorithm in terms of both the root mean square prediction error and the number of track loss. Soc. Inf. Fusion.
Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Information Fusion, FUSION 2000
PublisherIEEE Computer Society
Publication statusPublished - 1 Jan 2000
Event3rd International Conference on Information Fusion, FUSION 2000 - Paris, France
Duration: 10 Jul 200013 Jul 2000


Conference3rd International Conference on Information Fusion, FUSION 2000


  • adaptive IMM algorithm
  • fuzzy Kalman filter
  • IMM algorithm
  • model-set adaptation
  • Target tracking

ASJC Scopus subject areas

  • Information Systems


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