An intelligent tracking method for a maneuvering target

Bum Jik Lee, Young Hoon Joo, Jin Bae Park

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Accuracy in maneuvering target tracking using multiple models relies upon the suitability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.

Original languageEnglish
Pages (from-to)93-100
Number of pages8
JournalInternational Journal of Control, Automation and Systems
Issue number1
Publication statusPublished - 2003 Mar

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications


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