Parametric Bayesian Filters for Nonlinear Stochastic Dynamical Systems: A Survey

@article{Stano2013ParametricBF,
  title={Parametric Bayesian Filters for Nonlinear Stochastic Dynamical Systems: A Survey},
  author={Pawel Stano and Zs{\'o}fia Lendek and Jelmer Braaksma and Robert Babuska and Cees de Keizer and Arnold Jan den Dekker},
  journal={IEEE Transactions on Cybernetics},
  year={2013},
  volume={43},
  pages={1607-1624}
}
Nonlinear stochastic dynamical systems are commonly used to model physical processes. For linear and Gaussian systems, the Kalman filter is optimal in minimum mean squared error sense. However, for nonlinear or non-Gaussian systems, the estimation of states or parameters is a challenging problem. Furthermore, it is often required to process data online. Therefore, apart from being accurate, the feasible estimation algorithm also needs to be fast. In this paper, we review Bayesian filters that… CONTINUE READING
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