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Evolution Strategies Based Particle Filters for Nonlinear State Estimation

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Abstract

There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. By recognizing the similarities and the difference of the processes between the particle filters and Evolution Strategies, a new filter, Evolution Strategies Based Particle Filter, is proposed to circumvent this difficulty and to improve the performance. The applicability of the proposed idea is illustrated by numerical studies.

This work is partially supported by the Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (C)(2)14550447.

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Author information

Authors and Affiliations

  1. Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, 565-0871, Japan

    Katsuji Uosaki, Yuuya Kimura & Toshiharu Hatanaka

Authors
  1. Katsuji Uosaki
  2. Yuuya Kimura
  3. Toshiharu Hatanaka

Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Uosaki, K., Kimura, Y., Hatanaka, T. (2004). Evolution Strategies Based Particle Filters for Nonlinear State Estimation. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_161

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