A robust particle filter for state estimation — with convergence results

  title={A robust particle filter for state estimation — with convergence results},
  author={Xiao-Li Hu and Thomas B. Sch{\"o}n and Lennart Ljung},
  journal={2007 46th IEEE Conference on Decision and Control},
Particle filters are becoming increasingly important and useful for state estimation in nonlinear systems. Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itself has been lacking. This contribution describes a general framework for particle filters for state estimation, as well as a robustified filter version. For this version a quite general convergence… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.
2 Citations
14 References
Similar Papers


Publications citing this paper.
Showing 1-2 of 2 extracted citations


Publications referenced by this paper.
Showing 1-10 of 14 references

Feynman-Kac formulae: Genealogical and Interacting Particle Systems with Applications, ser. Probability and Applications

  • P. Del Moral
  • 2004
Highly Influential
4 Excerpts

Beyond the Kalman Filter: particle filters for tracking applications

  • B. Ristic, S. Arulampalam, N. Gordon
  • 2004
1 Excerpt

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

  • M. S. Arulampalam, S. Maskell, N. Gordon, T. Clapp
  • IEEE Transactions on Signal Processing, vol. 50…
  • 2002
1 Excerpt

Strategies in Scientific Computing, ser

  • J. S. Liu, Monte Carlo
  • 2001
1 Excerpt

Branching and Interacting Particle Systems Approximations of FeynmanKac Formulae with Applications to Non - Linear Filtering

  • P. Del Moral, L. Miclo
  • 2000

Similar Papers

Loading similar papers…