Novel adaptive particle filter using adjusted variance and its application

Sang Hyuk Park, Young Joong Kim, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Precise estimation of the position of robots, which is essential in mobile robotics, is difficult to achieve. However, particle filter shows great promise in this area. The number of samples used in this study is closely related to the operation time in particle filtering. The main issue in real-time implementation with regard to particle filter is to reduce the operation time, which led to the development of the adaptive particle filter (APF). We propose a new APF which adjusts the variance and then uses the gradient data to generate samples near the high likelihood region. The experiment results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler distance sampling.

Original languageEnglish
Pages (from-to)801-807
Number of pages7
JournalInternational Journal of Control, Automation and Systems
Issue number4
Publication statusPublished - 2010 Aug


  • Kullback-leibler distance
  • mobile robot
  • particle filter
  • ultrasonic beacon

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


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