A new evolutionary particle filter for the prevention of sample impoverishment

Seongkeun Park, Jae Pil Hwang, Euntai Kim, Hyung Jin Kang

Research output: Contribution to journalArticle

89 Citations (Scopus)


Particle filters perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. Unfortunately, there are some cases in which most particles are concentrated prematurely at a wrong point, thereby losing diversity and causing the estimation to fail. In this paper, genetic algorithms (GAs) are incorporated into a particle filter to overcome this drawback of the filter. By using genetic operators, the premature convergence of the particles is avoided and the search region of particles enlarged. The GA-inspired proposal distribution is proposed and the corresponding importance weight is derived to approximate the given target distribution. Finally, a computer simulation is performed to show the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)801-809
Number of pages9
JournalIEEE Transactions on Evolutionary Computation
Issue number4
Publication statusPublished - 2009

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics

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