제어로봇시스템학회:학술대회논문집

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Evolution Strategies Based Particle Filters for Nonlinear State Estimation

  • Uosaki, Katsuji (Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University) ;
  • Kimura, Yuuya (Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University) ;
  • Hatanaka, Toshiharu (Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University)
  • Published : 2003.10.22
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Abstract

Recently, particle filters have attracted attentions for nonlinear state estimation. They evaluate a posterior probability distribution of the state variable based on observations in simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. A new filter, Evolution Strategies Based Particle Filter, is proposed to circumvent this difficulty and to improve the performance. Numerical simulation results illustrate the applicability of the proposed idea.

Keywords