International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Prediction-based Interacting Multiple Model Estimation Algorithm for Target Tracking with Large Sampling Periods

  • Ryu, Jon-Ha (School of Electrical Engineering and Computer Science, Kyungpook National University) ;
  • Han, Du-Hee (Department of Electronics, Electrical, Control and Instrumentation Engineering, Hanyang University) ;
  • Lee, Kyun-Kyung (School of Electrical Engineering and Computer Science, Kyungpook National University) ;
  • Song, Taek-Lyul (Department of Electronics, Electrical, Control and Instrumentation Engineering, Hanyang University)
  • Published : 2008.02.28
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Abstract

An interacting multiple model (IMM) estimation algorithm based on the mixing of the predicted state estimates is proposed in this paper for a right continuous jump-linear system model different from the left-continuous system model used to develop the existing IMM algorithm. The difference lies in the modeling of the mode switching time. Performance of the proposed algorithm is compared numerically with that of the existing IMM algorithm for noisy system identification. Based on the numerical analysis, the proposed algorithm is applied to target tracking with a large sampling period for performance comparison with the existing IMM.

Keywords

References

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