A novel interacting multiple model algorithm based on multi-sensor optimal information fusion rule

  • Authors:

  • Xiaoyan Fu;Yingmin Jia;Junping Du;Shiying Yuan

  • Affiliations:

  • Seventh Research Division, Beihang University, Beijing, P.R. China;Seventh Research Division, Beihang University, Beijing, P.R. China;Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing, P.R. China;School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, Henan, P.R. China

  • Venue:
  • ACC'09 Proceedings of the 2009 conference on American Control Conference
  • Year:
  • 2009

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Abstract

In this paper, a novel interacting multiple model (IMM) algorithm is proposed, which utilizes a multi-sensor optimal information fusion rule to combine multiple models in the linear minimum variance sense instead of famous Bayes' rule. Furthermore, the diagonal matrices are used as the updated weights of models, which are applied to distinguish the effects produced by different dimensions of state, so the new algorithm is named as diagonal interacting multiple model (DIMM) algorithm. Extensive Monte Carlo simulations indicate that the proposed DIMM algorithm has better accuracy of estimation than the IMM algorithm with no increase in the execution time, which confirm that the DIMM algorithm is a competitive alternative to the classical IMM algorithm.