Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Graphical models for visual object recognition and tracking
Graphical models for visual object recognition and tracking
Iterative algorithms for state estimation of jump Markov linearsystems
IEEE Transactions on Signal Processing
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The Kalman filter (KF) models the propagation of uncertainty for a dynamic system where the noise distribution is Gaussian. This letter mainly explores the property of uncertainty propagation in the case where the noise property is unknown. The Dirichlet process mixture (DPM) model is employed to construct a general estimator of the noise distribution. Under the framework of nonparametric Bayes, we use the block sampling and KF techniques to approximate the posterior distribution of the noise. The simulation experiment shows that the proposed algorithm is efficient.