A Nonlinear Stochastic Filter for Continuous-Time State Estimation

@article{Ghoreyshi2015ANS,
  title={A Nonlinear Stochastic Filter for Continuous-Time State Estimation},
  author={Atiyeh Ghoreyshi and Terence D. Sanger},
  journal={IEEE Transactions on Automatic Control},
  year={2015},
  volume={60},
  pages={2161-2165}
}
  • Atiyeh Ghoreyshi, Terence D. Sanger
  • Published in
    IEEE Transactions on…
    2015
  • Medicine, Computer Science, Mathematics
  • Nonlinear filters produce a nonparametric estimate of the probability density of state at each point in time. Currently-known nonlinear filters include Particle Filters and the Kushner equation (and its un-normalized version: the Zakai equation). However, these filters have limited measurement models: Particle Filters require measurement at discrete times, and the Kushner and Zakai equations only apply when the measurement can be represented as a function of the state. We present a new… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Citations

    Publications citing this paper.
    SHOWING 1-7 OF 7 CITATIONS

    Fitting for smoothing: A methodology for continuous-time target track estimation

    VIEW 5 EXCERPTS
    CITES BACKGROUND
    HIGHLY INFLUENCED

    A short revisit of nonlinear Gaussian filters: State-of-the-art and some concerns

    Continuous-time estimation of latent variables from Poisson-spiking neurons

    VIEW 1 EXCERPT
    CITES METHODS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 15 REFERENCES