한국지능시스템학회:학술대회논문집 (Proceedings of the Korean Institute of Intelligent Systems Conference)

한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Radar Tracking Using a Fuzzy-Model-Based Kalman Filter

  • Lee, Bum-Jik (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Joo, Young-Hoon (School of Electronic and Information Engineering, Kunsan University) ;
  • Park, Jin-Bae (Department of Electrical and Electronic Engineering, Yonsei University)
  • 발행 : 2003.05.01
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초록

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

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